Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads
Background Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequenci...
Saved in:
| Published in: | BMC bioinformatics Vol. 15; no. 1; p. 182 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
12.06.2014
BioMed Central Ltd Springer Nature B.V |
| Subjects: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.
Results
To improve the efficiency of adapter trimming, we devised a novel algorithm, the
bit-masked k-difference matching algorithm
, which has
O
(
k
n
) expected time with
O
(
m
) space, where
k
is the maximum number of differences allowed,
n
is the read length, and
m
is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named
Skewer
(
https://sourceforge.net/projects/skewer
). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that
Skewer
outperforms all other similar tools that have the same utility. Further,
Skewer
is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.
Conclusions
Skewer
achieved as yet unmatched accuracies for adapter trimming with low time bound. |
|---|---|
| AbstractList | Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.BACKGROUNDAdapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.RESULTSTo improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound.CONCLUSIONSSkewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Background: Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. Results: To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(k n) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer ( https://sourceforge.net/projects/skewer ). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. Conclusions: Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Background Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. Results To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm , which has O ( k n ) expected time with O ( m ) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer ( https://sourceforge.net/projects/skewer ). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. Conclusions Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Background Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. Results To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer ( Conclusions Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Keywords: Next generation sequencing, Adapter trimming, Approximate string matching, Local sequence alignment, Barcode demultiplexing Doc number: 182 Abstract Background: Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. Results: To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm , which has O (k n ) expected time with O (m ) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer ). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. Conclusions: Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound. |
| ArticleNumber | 182 |
| Audience | Academic |
| Author | Ding, Shou-Wei Zhu, Shuifang Jiang, Hongshan Lei, Rong |
| AuthorAffiliation | 1 Institute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine, Huixinli 241, Beijing, 100029 China 2 Department of Plant Pathology and Microbiology and Institute for Integrative Biology, University of California, Riverside, 900 University Ave, 92521 Riverside, USA |
| AuthorAffiliation_xml | – name: 2 Department of Plant Pathology and Microbiology and Institute for Integrative Biology, University of California, Riverside, 900 University Ave, 92521 Riverside, USA – name: 1 Institute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine, Huixinli 241, Beijing, 100029 China |
| Author_xml | – sequence: 1 givenname: Hongshan surname: Jiang fullname: Jiang, Hongshan email: hongshan.jiang@gmail.com organization: Institute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine – sequence: 2 givenname: Rong surname: Lei fullname: Lei, Rong organization: Institute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine – sequence: 3 givenname: Shou-Wei surname: Ding fullname: Ding, Shou-Wei organization: Department of Plant Pathology and Microbiology and Institute for Integrative Biology, University of California – sequence: 4 givenname: Shuifang surname: Zhu fullname: Zhu, Shuifang organization: Institute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24925680$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNks1rFTEUxYNUbPt070oG3NjF1GS-krgQSqlaKAhWVy5CXnIzps7LPJOMH_-9d3xtfa-oSAIJye8cbm7OIdkLYwBCHjN6zJjonrOGs7JitC0ZTlHdIwe3R3tb-31ymNIVpYwL2j4g-1Ujq7YT9IB8vPwM3yC-KHThdMqFDrbQxkxRZyi01esMscjRr1a4ujEWAb7nsocASPgxFAm-TBCMD32x1j6CLQEtImibHpL7Tg8JHl2vC_Lh1dn70zflxdvX56cnF6XppMyldY1saFsZC1UtNWvBcQfSCrDOdZUTltd11TAtlqCNrFuLlTMhhamXlnNaL8jLje96Wq7AGgg56kGtsWodf6hRe7V7E_wn1Y9fVUN5U4sWDZ5dG8QRX5OyWvlkYBh0gHFKirUNFx2nHfsftKJUzsYL8vQOejVOMWAnZopVUsiu_k31egDlgxuxRDObqpO2lk1HheBIHf-BwmFh5Q2Gwnk83xEc7QiQyfhzvZ5SUueX73bZJ9v9u23cTUoQ6DaAiWNKEZwyPv_6fqzCD4pRNcdRzXlTc97wgQrjiEJ6R3jj_Q8J20gSoqGHuNW1v2l-ArWa7d4 |
| CitedBy_id | crossref_primary_10_1186_s13020_020_0298_x crossref_primary_10_7554_eLife_49117 crossref_primary_10_1007_s11738_020_3013_8 crossref_primary_10_1007_s00401_024_02694_1 crossref_primary_10_3389_fmars_2024_1529684 crossref_primary_10_1016_j_ygeno_2024_110858 crossref_primary_10_1007_s00253_023_12863_z crossref_primary_10_1016_j_molcel_2022_12_029 crossref_primary_10_1016_j_cmet_2018_01_014 crossref_primary_10_1007_s12264_024_01324_w crossref_primary_10_1016_j_molcel_2019_04_008 crossref_primary_10_1128_Spectrum_00287_21 crossref_primary_10_1038_s42003_024_07310_2 crossref_primary_10_1128_IAI_00270_21 crossref_primary_10_3389_fpls_2022_857016 crossref_primary_10_1038_s41396_020_0665_8 crossref_primary_10_3389_fimmu_2023_1158493 crossref_primary_10_1111_plb_12955 crossref_primary_10_1093_gigascience_gix120 crossref_primary_10_1038_s41467_025_57363_y crossref_primary_10_1038_s41467_021_24575_x crossref_primary_10_1002_1873_3468_13549 crossref_primary_10_1002_jmv_24839 crossref_primary_10_1093_nar_gkaa500 crossref_primary_10_1136_bmjdrc_2021_002285 crossref_primary_10_1210_endocr_bqac031 crossref_primary_10_1128_mra_00957_22 crossref_primary_10_3389_fmicb_2022_890686 crossref_primary_10_1093_bib_bbab030 crossref_primary_10_1111_mms_13195 crossref_primary_10_1038_s41525_024_00390_3 crossref_primary_10_1016_j_ymthe_2024_04_029 crossref_primary_10_1080_23802359_2020_1749157 crossref_primary_10_1016_j_celrep_2025_115673 crossref_primary_10_1371_journal_pone_0222004 crossref_primary_10_3389_fpls_2019_01249 crossref_primary_10_1038_s41398_019_0517_3 crossref_primary_10_3390_jof8010061 crossref_primary_10_1038_s41598_020_68394_4 crossref_primary_10_1038_s41536_023_00316_0 crossref_primary_10_1038_s41467_024_49473_w crossref_primary_10_1038_s41586_022_04416_7 crossref_primary_10_3390_v16101538 crossref_primary_10_1128_mra_01162_24 crossref_primary_10_1080_23802359_2020_1749167 crossref_primary_10_1016_j_ebiom_2024_105453 crossref_primary_10_1016_j_gene_2024_148984 crossref_primary_10_1016_j_molcel_2022_11_002 crossref_primary_10_1080_23802359_2020_1749166 crossref_primary_10_3390_genes12101622 crossref_primary_10_1038_s41467_022_34063_5 crossref_primary_10_1186_s13020_021_00502_6 crossref_primary_10_1038_s41593_020_0650_6 crossref_primary_10_1242_dmm_049463 crossref_primary_10_1038_s41536_019_0079_2 crossref_primary_10_1038_s41467_025_63031_y crossref_primary_10_1016_j_molcel_2025_01_017 crossref_primary_10_1016_j_cell_2021_05_009 crossref_primary_10_3390_ijms231911191 crossref_primary_10_3390_cells11203266 crossref_primary_10_1093_protein_gzz022 crossref_primary_10_1038_s41435_020_00118_0 crossref_primary_10_1038_srep45125 crossref_primary_10_3389_fpls_2021_645735 crossref_primary_10_1007_s12264_024_01302_2 crossref_primary_10_1186_s13024_022_00547_7 crossref_primary_10_1111_eva_13229 crossref_primary_10_1016_j_dnarep_2021_103172 crossref_primary_10_1038_s41598_018_28217_z crossref_primary_10_1038_s41598_021_90938_5 crossref_primary_10_7717_peerj_7170 crossref_primary_10_1186_s12866_025_04146_6 crossref_primary_10_3389_fmicb_2021_743126 crossref_primary_10_1186_s12881_019_0795_x crossref_primary_10_1007_s12672_020_00377_3 crossref_primary_10_1016_j_stem_2023_12_012 crossref_primary_10_1073_pnas_2016950118 crossref_primary_10_3389_fonc_2023_1200387 crossref_primary_10_1007_s11033_025_11060_7 crossref_primary_10_3390_ijms222212461 crossref_primary_10_1093_nar_gky171 crossref_primary_10_3897_vz_74_e125958 crossref_primary_10_3389_fped_2021_649043 crossref_primary_10_3390_biology11010068 crossref_primary_10_1016_j_jss_2022_04_017 crossref_primary_10_3390_ijms21186762 crossref_primary_10_1038_s41467_020_16051_9 crossref_primary_10_3390_biomedicines9010056 crossref_primary_10_1016_j_indcrop_2022_115379 crossref_primary_10_1111_mec_17271 crossref_primary_10_1186_s13046_024_03211_8 crossref_primary_10_3389_fsufs_2021_644230 crossref_primary_10_2217_fon_2020_1183 crossref_primary_10_1016_j_psj_2022_102097 crossref_primary_10_1371_journal_pone_0280354 crossref_primary_10_1016_j_celrep_2025_115237 crossref_primary_10_1038_srep20200 crossref_primary_10_1371_journal_pgen_1006404 crossref_primary_10_1038_s41419_017_0046_z crossref_primary_10_1093_nar_gkaa551 crossref_primary_10_1016_j_chom_2021_08_001 crossref_primary_10_1038_s41467_021_24967_z crossref_primary_10_1038_s41598_018_20372_7 crossref_primary_10_1038_s41591_021_01623_z crossref_primary_10_1016_j_stem_2020_06_015 crossref_primary_10_1073_pnas_2101164118 crossref_primary_10_1016_j_molcel_2024_08_030 crossref_primary_10_2196_50733 crossref_primary_10_3389_fmicb_2019_02461 crossref_primary_10_3390_genes11010050 crossref_primary_10_1128_mra_01070_22 crossref_primary_10_1128_MRA_01255_19 crossref_primary_10_3389_fmicb_2021_705679 crossref_primary_10_1038_s41467_022_34899_x crossref_primary_10_1038_s41598_017_13347_7 crossref_primary_10_1158_0008_5472_CAN_25_0999 crossref_primary_10_1101_gr_198473_115 crossref_primary_10_1038_s41467_021_21021_w crossref_primary_10_1093_genetics_iyae032 crossref_primary_10_1134_S1022795424701795 crossref_primary_10_1371_journal_pbio_2002266 crossref_primary_10_3390_cells10081923 crossref_primary_10_1007_s11230_023_10106_3 crossref_primary_10_1186_s13104_016_1900_2 crossref_primary_10_1038_s41564_022_01252_3 crossref_primary_10_1101_gr_259044_119 crossref_primary_10_1371_journal_ppat_1009861 crossref_primary_10_1099_jgv_0_001749 crossref_primary_10_1128_AAC_01324_19 crossref_primary_10_1111_nph_20463 crossref_primary_10_1128_mbio_01320_25 crossref_primary_10_1111_mec_14892 crossref_primary_10_3390_ijms251910698 crossref_primary_10_1016_j_yexcr_2023_113819 crossref_primary_10_1093_nar_gkx1263 crossref_primary_10_1084_jem_20201354 crossref_primary_10_1371_journal_pone_0209694 crossref_primary_10_1016_j_jid_2023_11_006 crossref_primary_10_1038_s41588_021_00880_5 crossref_primary_10_1038_s41598_023_29782_8 crossref_primary_10_1007_s10530_024_03386_3 crossref_primary_10_1093_molbev_msaf057 crossref_primary_10_1093_nar_gkad612 crossref_primary_10_3389_fpls_2025_1543229 crossref_primary_10_1038_s41598_019_46550_9 crossref_primary_10_1038_s41591_024_03040_4 crossref_primary_10_5114_bta_2023_132775 crossref_primary_10_1093_brain_awab403 crossref_primary_10_1371_journal_pcbi_1011001 crossref_primary_10_1111_andr_12832 crossref_primary_10_7554_eLife_66063 crossref_primary_10_1186_s12864_021_07788_8 crossref_primary_10_1371_journal_pone_0247928 crossref_primary_10_1016_j_jaci_2022_01_028 crossref_primary_10_1038_s44161_023_00219_9 crossref_primary_10_1093_ismeco_ycae112 crossref_primary_10_1016_j_aquatox_2019_105230 crossref_primary_10_1016_j_ijantimicag_2019_03_001 crossref_primary_10_1093_ve_veac065 crossref_primary_10_1371_journal_ppat_1009602 crossref_primary_10_1016_j_cell_2024_02_023 crossref_primary_10_1038_s41590_024_01814_z crossref_primary_10_3390_cancers13122954 crossref_primary_10_1002_ps_8775 crossref_primary_10_1002_mbo3_1286 crossref_primary_10_1186_s43556_020_00023_y crossref_primary_10_1371_journal_pone_0266430 crossref_primary_10_1186_s12862_021_01773_1 crossref_primary_10_1080_21505594_2024_2348251 crossref_primary_10_3390_vetsci12020099 crossref_primary_10_3389_fphar_2021_746910 crossref_primary_10_1093_zoolinnean_zlad063 crossref_primary_10_1128_mSphere_01071_20 crossref_primary_10_1038_s41590_019_0343_z crossref_primary_10_1002_ece3_5804 crossref_primary_10_1534_genetics_120_303231 crossref_primary_10_1186_s12931_022_02156_w crossref_primary_10_1016_j_indcrop_2020_112567 crossref_primary_10_1038_s41698_022_00298_0 crossref_primary_10_1093_nar_gkac356 crossref_primary_10_1016_j_jse_2025_06_004 crossref_primary_10_7554_eLife_10005 crossref_primary_10_1038_s44318_024_00035_2 crossref_primary_10_3390_v13060982 crossref_primary_10_1016_j_funeco_2020_100921 crossref_primary_10_1016_j_immuni_2019_12_002 crossref_primary_10_1136_gutjnl_2024_333353 crossref_primary_10_3390_ijms26010056 crossref_primary_10_1038_s41467_020_18781_2 crossref_primary_10_1182_blood_2021011798 crossref_primary_10_1038_s41698_025_00860_6 crossref_primary_10_1007_s00204_020_02969_y crossref_primary_10_3390_plants13233439 crossref_primary_10_3389_fgene_2021_699280 crossref_primary_10_1093_molbev_msz010 crossref_primary_10_1002_adma_202306158 crossref_primary_10_1038_s41598_020_67679_y crossref_primary_10_1007_s10295_020_02328_x crossref_primary_10_1186_s12920_019_0557_9 crossref_primary_10_3389_fmicb_2021_812436 crossref_primary_10_3390_ph16121723 crossref_primary_10_1515_jpm_2025_0214 crossref_primary_10_1016_j_chom_2019_03_001 crossref_primary_10_1016_j_yexcr_2019_03_028 crossref_primary_10_12688_wellcomeopenres_19680_1 crossref_primary_10_12688_wellcomeopenres_19680_2 crossref_primary_10_1186_s12915_023_01665_4 crossref_primary_10_3389_fgene_2021_740437 crossref_primary_10_7554_eLife_70619 crossref_primary_10_1371_journal_pone_0198957 crossref_primary_10_1016_j_omtm_2020_03_024 crossref_primary_10_3389_fcell_2024_1344070 crossref_primary_10_7554_eLife_59974 crossref_primary_10_3389_fmicb_2024_1476845 crossref_primary_10_1073_pnas_1911024116 crossref_primary_10_1186_s13068_025_02688_5 crossref_primary_10_1016_j_virol_2023_02_014 crossref_primary_10_1159_000542220 crossref_primary_10_1371_journal_pgen_1010063 crossref_primary_10_2196_30291 crossref_primary_10_1016_j_xgen_2025_100780 crossref_primary_10_1146_annurev_phyto_080614_120030 crossref_primary_10_3389_fcimb_2022_922031 crossref_primary_10_1038_s41467_022_33176_1 crossref_primary_10_3389_fmicb_2016_01213 crossref_primary_10_1038_s41467_018_06353_4 crossref_primary_10_1126_scitranslmed_adk0642 crossref_primary_10_1016_j_cub_2024_04_050 crossref_primary_10_1038_s41598_024_74002_6 crossref_primary_10_1016_j_ympev_2018_12_032 crossref_primary_10_1111_1462_2920_16578 crossref_primary_10_3390_jof10030180 crossref_primary_10_1016_j_ecoenv_2019_110116 crossref_primary_10_1016_j_vaccine_2023_07_019 crossref_primary_10_1111_mec_15231 crossref_primary_10_1371_journal_pbio_2004830 crossref_primary_10_1016_j_molcel_2018_06_015 crossref_primary_10_1126_science_aav1898 crossref_primary_10_1126_sciimmunol_ads1556 crossref_primary_10_1016_j_ympev_2023_107708 crossref_primary_10_3390_cells14110774 crossref_primary_10_1038_s41588_018_0278_6 crossref_primary_10_1016_j_cell_2025_03_031 crossref_primary_10_1093_femsyr_fox049 crossref_primary_10_1183_23120541_00150_2017 crossref_primary_10_1016_j_ympev_2025_108357 crossref_primary_10_7554_eLife_35989 crossref_primary_10_1289_EHP3281 crossref_primary_10_3389_fcell_2021_663032 crossref_primary_10_1128_mra_00001_25 crossref_primary_10_1186_s13023_022_02424_4 crossref_primary_10_1371_journal_pone_0206586 crossref_primary_10_1371_journal_pone_0173421 crossref_primary_10_3390_genes16070739 crossref_primary_10_1038_s41598_020_78876_0 crossref_primary_10_1126_scitranslmed_adj7308 crossref_primary_10_1139_cjb_2020_0014 crossref_primary_10_1167_iovs_63_9_4 crossref_primary_10_3201_eid2711_210124 crossref_primary_10_1016_j_jbc_2023_104768 crossref_primary_10_1126_science_aam9425 crossref_primary_10_1093_femsec_fiae140 crossref_primary_10_1016_j_plaphy_2019_12_035 crossref_primary_10_1002_humu_24064 crossref_primary_10_1038_s41390_020_01347_9 crossref_primary_10_3390_biology11111681 crossref_primary_10_1128_spectrum_01906_24 crossref_primary_10_1038_s41398_017_0022_5 crossref_primary_10_1186_s12864_017_3840_1 crossref_primary_10_1038_s41598_020_76313_w crossref_primary_10_1371_journal_pone_0231752 crossref_primary_10_1016_j_molcel_2022_01_024 crossref_primary_10_1186_s12864_016_3445_0 crossref_primary_10_1371_journal_pgen_1008229 crossref_primary_10_1093_jhered_esaa031 crossref_primary_10_1128_MRA_00618_23 crossref_primary_10_1073_pnas_1718406115 crossref_primary_10_1128_mra_00575_24 crossref_primary_10_1016_j_ecoenv_2019_110132 crossref_primary_10_1016_j_neuron_2019_12_007 crossref_primary_10_1016_j_ccell_2025_02_014 crossref_primary_10_1126_scitranslmed_abj3860 crossref_primary_10_3390_ijms252413349 crossref_primary_10_1186_s12859_016_1069_7 crossref_primary_10_1016_j_cub_2020_01_064 crossref_primary_10_1126_sciimmunol_aay3994 crossref_primary_10_1111_gbb_12619 crossref_primary_10_1186_s12915_022_01425_w crossref_primary_10_1099_mgen_0_000492 crossref_primary_10_1016_j_plaphy_2019_12_019 crossref_primary_10_3390_medicina61091649 crossref_primary_10_1038_s41598_020_77606_w crossref_primary_10_1038_s41598_018_19430_x crossref_primary_10_1371_journal_pgen_1010770 crossref_primary_10_3389_fmicb_2018_01581 crossref_primary_10_1016_j_celrep_2023_113322 crossref_primary_10_1038_s41598_020_67798_6 crossref_primary_10_1016_j_ebiom_2023_104778 crossref_primary_10_1038_s41598_019_38532_8 crossref_primary_10_1002_pmic_201700359 crossref_primary_10_1016_j_jviromet_2019_113703 crossref_primary_10_1093_jac_dky291 crossref_primary_10_1128_aem_01320_23 crossref_primary_10_1371_journal_pone_0287524 crossref_primary_10_1016_j_jcz_2021_06_002 crossref_primary_10_3390_life11060540 crossref_primary_10_3390_ijms26125839 crossref_primary_10_3390_ijms24054262 crossref_primary_10_1038_s41588_021_00875_2 crossref_primary_10_1080_23802359_2021_1944388 crossref_primary_10_1038_s43018_024_00787_0 crossref_primary_10_1038_s41590_021_00928_y crossref_primary_10_3390_genes13091664 crossref_primary_10_1098_rsos_201788 crossref_primary_10_1099_jgv_0_001307 crossref_primary_10_1038_s41396_019_0438_4 crossref_primary_10_1126_science_aaz5284 crossref_primary_10_1016_j_ympev_2024_108282 crossref_primary_10_1186_s13099_021_00409_5 crossref_primary_10_3390_ijms22094936 crossref_primary_10_1016_j_ympev_2024_108043 crossref_primary_10_1515_jpm_2024_0310 crossref_primary_10_1016_j_ebiom_2023_104555 crossref_primary_10_3389_fgene_2018_00094 crossref_primary_10_3390_plants13131765 crossref_primary_10_1016_j_cub_2025_03_026 crossref_primary_10_1111_mec_15891 crossref_primary_10_1016_j_nbd_2024_106469 crossref_primary_10_1099_mgen_0_000434 crossref_primary_10_1038_s41592_025_02648_9 crossref_primary_10_1016_j_cbd_2020_100714 crossref_primary_10_1016_j_nbd_2024_106460 crossref_primary_10_1038_s41467_024_54543_0 crossref_primary_10_15252_embj_2019103209 crossref_primary_10_7717_peerj_5428 crossref_primary_10_3390_ijms25021115 crossref_primary_10_1186_s13104_024_07035_9 crossref_primary_10_1089_omi_2023_0078 crossref_primary_10_1016_j_stem_2024_08_007 crossref_primary_10_1038_s41419_021_03539_5 crossref_primary_10_1016_j_margen_2019_03_004 crossref_primary_10_1016_j_devcel_2018_12_026 crossref_primary_10_3389_fgene_2022_764534 crossref_primary_10_1038_s41598_018_37177_3 crossref_primary_10_1038_s41564_019_0513_7 crossref_primary_10_1038_s41588_019_0477_9 crossref_primary_10_1038_s41467_020_15047_9 crossref_primary_10_1016_j_ympev_2024_108237 crossref_primary_10_1128_mra_00101_24 crossref_primary_10_1016_j_devcel_2017_02_021 crossref_primary_10_1128_mSphere_00354_17 crossref_primary_10_1159_000539994 crossref_primary_10_1126_sciimmunol_aas9822 crossref_primary_10_1186_s40850_017_0013_2 crossref_primary_10_1038_s41467_023_40315_9 crossref_primary_10_1093_nar_gkz855 crossref_primary_10_3389_fmicb_2023_1092216 crossref_primary_10_2217_epi_2023_0351 crossref_primary_10_3389_fonc_2021_650891 crossref_primary_10_1371_journal_pone_0205407 crossref_primary_10_1016_j_jid_2019_09_026 crossref_primary_10_3390_cells13201735 crossref_primary_10_1534_genetics_116_194050 crossref_primary_10_1093_hmg_ddac010 crossref_primary_10_3390_genes15080996 crossref_primary_10_3389_fgene_2021_593515 crossref_primary_10_1002_ana_25480 crossref_primary_10_1016_j_apsb_2022_11_015 crossref_primary_10_1002_path_6304 crossref_primary_10_1016_j_humpath_2021_10_002 crossref_primary_10_3390_genes13020242 crossref_primary_10_15252_emmm_202217175 crossref_primary_10_1007_s10592_025_01695_1 crossref_primary_10_1093_gigascience_giz022 crossref_primary_10_3389_fmicb_2018_03140 crossref_primary_10_1128_msystems_01368_23 crossref_primary_10_3389_fmicb_2020_573857 crossref_primary_10_1371_journal_pone_0216992 crossref_primary_10_1016_j_omtn_2022_04_031 crossref_primary_10_1016_j_stem_2025_02_013 crossref_primary_10_1007_s13311_021_01024_7 crossref_primary_10_1007_s00425_023_04223_y crossref_primary_10_1016_j_jaridenv_2020_104316 crossref_primary_10_1128_MRA_00736_21 crossref_primary_10_1099_mgen_0_000872 crossref_primary_10_1371_journal_pntd_0011499 crossref_primary_10_3389_fcell_2023_1176115 crossref_primary_10_3390_molecules23102426 crossref_primary_10_1093_gigascience_gix093 crossref_primary_10_1126_sciimmunol_adg3517 crossref_primary_10_1016_j_jgeb_2023_100341 crossref_primary_10_1128_MRA_00949_20 crossref_primary_10_3390_cancers12051101 crossref_primary_10_3390_ijms25042259 crossref_primary_10_1186_s12915_024_02073_y crossref_primary_10_1111_ajt_16244 crossref_primary_10_1038_s41467_019_11237_2 crossref_primary_10_1016_j_immuni_2023_12_005 crossref_primary_10_1038_s41423_019_0218_0 crossref_primary_10_3390_genes16090993 crossref_primary_10_1038_s41598_019_40285_3 crossref_primary_10_1038_s41467_025_62874_9 crossref_primary_10_1038_s42003_021_01846_3 crossref_primary_10_1111_1748_5967_12164 crossref_primary_10_1002_JLB_2MA1119_300R crossref_primary_10_3389_fpls_2023_1103857 crossref_primary_10_3389_fonc_2021_752579 crossref_primary_10_1097_ALN_0000000000004874 crossref_primary_10_3389_fphar_2024_1466578 crossref_primary_10_1136_jitc_2023_006821 crossref_primary_10_1186_s12958_021_00715_2 crossref_primary_10_1093_hmg_ddz014 crossref_primary_10_1038_s41467_022_33994_3 crossref_primary_10_1242_dmm_049068 crossref_primary_10_4049_jimmunol_2001028 crossref_primary_10_1093_bib_bbaa083 crossref_primary_10_2337_db19_0043 crossref_primary_10_1016_j_cell_2023_12_032 crossref_primary_10_1093_gastro_goaf015 crossref_primary_10_1186_s13059_025_03691_7 crossref_primary_10_1186_s13046_025_03356_0 crossref_primary_10_1534_g3_116_036194 crossref_primary_10_3390_v15030735 crossref_primary_10_1038_s41598_018_24126_3 crossref_primary_10_1093_molbev_msab268 crossref_primary_10_3390_vaccines13090943 crossref_primary_10_1038_s41467_019_14081_6 crossref_primary_10_3390_ijms26125432 crossref_primary_10_1038_s41467_022_32041_5 crossref_primary_10_3389_fimmu_2024_1437391 crossref_primary_10_3390_genes10010009 crossref_primary_10_3389_fimmu_2023_1294565 crossref_primary_10_1038_s41598_024_56480_w crossref_primary_10_1111_1755_0998_12768 crossref_primary_10_1111_1755_0998_12769 crossref_primary_10_1101_gad_324715_119 crossref_primary_10_3389_fmicb_2023_1080017 crossref_primary_10_3390_cancers11101485 crossref_primary_10_3390_v15030747 crossref_primary_10_1038_s41438_021_00641_9 crossref_primary_10_1007_s10123_023_00372_y crossref_primary_10_1111_mec_15257 crossref_primary_10_1038_s44161_022_00128_3 crossref_primary_10_1007_s10681_016_1655_9 crossref_primary_10_1161_ATVBAHA_119_313800 crossref_primary_10_1186_s12920_021_01045_3 crossref_primary_10_1128_MRA_00761_21 crossref_primary_10_1128_spectrum_00724_24 crossref_primary_10_1038_s41597_019_0313_1 crossref_primary_10_1080_23802359_2017_1413307 crossref_primary_10_1111_1462_2920_15910 crossref_primary_10_1186_s12859_019_2961_8 crossref_primary_10_1038_s41598_024_84163_z crossref_primary_10_21926_obm_genet_2503300 crossref_primary_10_7554_eLife_52896 crossref_primary_10_1038_s41597_023_02598_x crossref_primary_10_1093_plphys_kiab264 crossref_primary_10_1007_s12035_023_03866_y crossref_primary_10_1186_s12920_017_0295_9 crossref_primary_10_1016_j_cell_2018_08_067 crossref_primary_10_3390_plants9070820 crossref_primary_10_1182_blood_2020005780 crossref_primary_10_3390_ijms26073145 crossref_primary_10_1080_23802359_2020_1768940 crossref_primary_10_1038_s41588_019_0413_z crossref_primary_10_1016_j_cell_2018_07_029 crossref_primary_10_3390_cells11152342 crossref_primary_10_3390_cancers11101466 crossref_primary_10_1530_REP_22_0192 crossref_primary_10_3390_ijms26125648 crossref_primary_10_1007_s13562_020_00575_8 crossref_primary_10_1186_s12915_019_0711_z crossref_primary_10_3390_w14162456 crossref_primary_10_1002_ps_7370 crossref_primary_10_1093_nar_gkac822 crossref_primary_10_1007_s13127_024_00659_6 crossref_primary_10_1038_s41698_025_00854_4 crossref_primary_10_3390_ijms21093307 crossref_primary_10_1128_mra_01081_23 crossref_primary_10_1534_genetics_117_300679 crossref_primary_10_3390_cancers12061574 crossref_primary_10_21307_jofnem_2021_065 crossref_primary_10_3390_agronomy11101908 crossref_primary_10_1038_ncomms14529 crossref_primary_10_3390_ijms19072052 crossref_primary_10_1016_j_ymeth_2022_05_001 crossref_primary_10_7554_eLife_47040 crossref_primary_10_1016_j_aquatox_2020_105443 crossref_primary_10_1093_zoolinnean_zlad158 crossref_primary_10_3390_nu13051457 crossref_primary_10_1016_j_ijantimicag_2023_106747 crossref_primary_10_1093_nargab_lqab101 crossref_primary_10_1371_journal_pone_0239804 crossref_primary_10_3390_ijms241914833 crossref_primary_10_1038_s41586_022_05202_1 crossref_primary_10_1093_g3journal_jkab367 crossref_primary_10_1038_s41588_019_0493_9 crossref_primary_10_3390_genes12122007 crossref_primary_10_3390_v12111286 crossref_primary_10_1261_rna_080215_124 crossref_primary_10_1186_s13287_025_04280_y crossref_primary_10_1371_journal_pone_0192898 crossref_primary_10_1016_j_cell_2023_02_017 crossref_primary_10_1038_s42255_021_00386_8 crossref_primary_10_1186_s12931_022_02077_8 crossref_primary_10_3390_microorganisms13071532 crossref_primary_10_1186_s13059_018_1458_5 crossref_primary_10_7717_peerj_3720 crossref_primary_10_1007_s00203_023_03715_5 crossref_primary_10_3389_fmicb_2024_1407904 crossref_primary_10_1126_scitranslmed_aay1163 crossref_primary_10_1111_febs_16595 crossref_primary_10_1038_s41598_019_43141_6 crossref_primary_10_3390_min10030208 crossref_primary_10_3390_nu16070942 crossref_primary_10_1007_s00251_021_01251_4 crossref_primary_10_1080_22221751_2024_2322655 crossref_primary_10_3390_md21120621 crossref_primary_10_1007_s10295_018_2087_4 crossref_primary_10_1126_science_adn5606 crossref_primary_10_1186_s12964_025_02184_1 crossref_primary_10_1152_japplphysiol_00432_2025 crossref_primary_10_1111_evo_13581 crossref_primary_10_1007_s10815_025_03509_2 crossref_primary_10_3389_fmicb_2019_00120 crossref_primary_10_3389_fmicb_2024_1324403 crossref_primary_10_3389_fpls_2020_563187 crossref_primary_10_1038_s41589_024_01635_z crossref_primary_10_1038_s42003_021_02412_7 crossref_primary_10_3390_ani12233328 crossref_primary_10_1038_s41598_020_61273_y crossref_primary_10_1038_s41467_018_07394_5 crossref_primary_10_3389_fmicb_2023_1239538 crossref_primary_10_3390_cancers11091329 crossref_primary_10_7554_eLife_52611 crossref_primary_10_1016_S1875_5364_20_30068_6 crossref_primary_10_1128_mra_00776_25 crossref_primary_10_1080_19490976_2021_1902771 crossref_primary_10_1186_s12867_018_0108_5 crossref_primary_10_1093_femsec_fiaa143 crossref_primary_10_5713_ab_21_0420 crossref_primary_10_1016_j_tranon_2018_04_002 crossref_primary_10_1038_s41586_021_03795_7 crossref_primary_10_1038_s41587_024_02313_0 crossref_primary_10_1186_s12864_017_3529_5 crossref_primary_10_1073_pnas_1907787116 crossref_primary_10_1038_s41419_022_05021_2 crossref_primary_10_1111_mec_17173 crossref_primary_10_1371_journal_pgen_1009813 crossref_primary_10_1016_j_vetmic_2022_109459 crossref_primary_10_3390_microorganisms13040854 crossref_primary_10_1016_j_bbr_2019_02_043 crossref_primary_10_1016_j_jaci_2023_01_006 crossref_primary_10_1111_pbi_13635 crossref_primary_10_1038_s41598_020_70249_x crossref_primary_10_1038_s41591_018_0078_7 crossref_primary_10_1136_jitc_2024_011070 crossref_primary_10_3389_fnmol_2020_00043 crossref_primary_10_1111_nph_16281 crossref_primary_10_1186_s13148_024_01623_z crossref_primary_10_1093_gigascience_gix035 crossref_primary_10_1016_j_ccell_2023_01_004 crossref_primary_10_1093_gigascience_giy132 crossref_primary_10_3390_plants10061092 crossref_primary_10_1111_bjh_20089 crossref_primary_10_1016_j_envpol_2018_09_034 crossref_primary_10_1128_mra_00922_24 crossref_primary_10_1186_s13567_025_01566_0 crossref_primary_10_1038_s41591_020_0915_3 crossref_primary_10_1038_s41598_025_96204_2 crossref_primary_10_1016_j_ydbio_2020_07_016 crossref_primary_10_1007_s13353_020_00603_2 crossref_primary_10_1038_s41467_022_28973_7 crossref_primary_10_1038_s41563_019_0287_6 crossref_primary_10_1038_s41588_024_01668_z crossref_primary_10_1128_aac_00735_23 crossref_primary_10_1016_j_jaci_2023_02_028 crossref_primary_10_3390_cells9061534 crossref_primary_10_1038_s41388_024_03026_z crossref_primary_10_12688_f1000research_124724_1 crossref_primary_10_1038_s41467_023_37783_4 crossref_primary_10_1038_s42003_024_07106_4 crossref_primary_10_3390_ijms23031199 crossref_primary_10_1093_molbev_msz188 crossref_primary_10_1016_j_omtn_2016_12_001 crossref_primary_10_1093_genetics_iyac117 crossref_primary_10_4049_jimmunol_1900856 crossref_primary_10_1038_s41586_022_04962_0 crossref_primary_10_1016_j_molp_2022_05_013 crossref_primary_10_1038_s41467_019_09276_w crossref_primary_10_1016_j_aquatox_2020_105462 crossref_primary_10_1136_gutjnl_2021_326314 crossref_primary_10_1007_s11295_020_01454_y crossref_primary_10_3897_vz_75_e150370 crossref_primary_10_1016_S1002_0160_17_60357_6 crossref_primary_10_1093_g3journal_jkab344 crossref_primary_10_3390_ijms25179638 crossref_primary_10_1016_j_ijpara_2017_11_008 crossref_primary_10_1371_journal_pone_0328922 crossref_primary_10_1371_journal_pone_0163235 crossref_primary_10_1016_j_cell_2020_06_006 crossref_primary_10_1016_j_ymthe_2020_05_019 crossref_primary_10_1128_AAC_00554_20 crossref_primary_10_1177_10732748251327720 crossref_primary_10_1007_s00425_024_04454_7 crossref_primary_10_1016_j_ejca_2024_114314 crossref_primary_10_1261_rna_075523_120 crossref_primary_10_1038_s41467_019_08637_9 crossref_primary_10_1017_S0031182024000386 crossref_primary_10_3389_fimmu_2019_00116 crossref_primary_10_3389_fvets_2024_1463342 crossref_primary_10_1186_s12864_023_09786_4 crossref_primary_10_1016_j_ygeno_2022_110333 crossref_primary_10_1128_MRA_00616_21 crossref_primary_10_1007_s13105_024_01023_0 crossref_primary_10_1038_s41467_022_29551_7 crossref_primary_10_1371_journal_pone_0220365 crossref_primary_10_1016_j_ygeno_2020_11_034 crossref_primary_10_1093_ibd_izab116 crossref_primary_10_1186_s12934_017_0679_8 crossref_primary_10_1016_j_cub_2025_03_013 crossref_primary_10_1038_s41598_018_29305_w crossref_primary_10_1186_s13104_024_06951_0 crossref_primary_10_1111_pbi_13838 crossref_primary_10_1186_s13059_024_03187_w crossref_primary_10_5187_jast_2022_e127 crossref_primary_10_1186_s12885_017_3815_2 crossref_primary_10_3389_fgene_2021_656061 crossref_primary_10_1111_gbb_12908 crossref_primary_10_1038_s41564_019_0531_5 crossref_primary_10_1094_PDIS_10_24_2151_SC crossref_primary_10_3389_fgeed_2022_1031275 crossref_primary_10_1038_s42003_021_02079_0 crossref_primary_10_1093_nar_gkad773 crossref_primary_10_1016_j_omtn_2019_12_012 crossref_primary_10_3390_ijms25116054 crossref_primary_10_3390_cancers16183158 crossref_primary_10_1111_mec_14525 crossref_primary_10_1371_journal_ppat_1009732 crossref_primary_10_1101_gr_277698_123 crossref_primary_10_3389_fmicb_2016_00032 crossref_primary_10_1038_nmeth_3542 crossref_primary_10_1093_femsyr_foac053 crossref_primary_10_1371_journal_pone_0178706 crossref_primary_10_1093_gbe_evaf037 crossref_primary_10_3390_jpm11030175 crossref_primary_10_1016_j_stem_2022_03_009 crossref_primary_10_1016_j_stem_2018_06_014 crossref_primary_10_3389_fmars_2022_784807 crossref_primary_10_1128_AEM_02346_18 crossref_primary_10_3390_cimb46110778 crossref_primary_10_3389_fimmu_2024_1475126 crossref_primary_10_1038_s41467_024_52464_6 crossref_primary_10_1093_nargab_lqac054 crossref_primary_10_1002_lno_11257 crossref_primary_10_3390_plants12233995 crossref_primary_10_1186_s13072_025_00579_5 crossref_primary_10_1158_0008_5472_CAN_20_1518 crossref_primary_10_1016_j_scitotenv_2022_156486 crossref_primary_10_1089_crispr_2022_0072 crossref_primary_10_1038_srep13426 crossref_primary_10_1038_s41588_021_00904_0 crossref_primary_10_1186_s13028_022_00653_y crossref_primary_10_1371_journal_pone_0291995 crossref_primary_10_3390_ijms241713646 crossref_primary_10_3390_pathogens9121031 crossref_primary_10_1016_j_jbiotec_2017_06_1203 crossref_primary_10_1128_mra_01104_22 crossref_primary_10_1186_s13072_017_0147_z crossref_primary_10_1371_journal_ppat_1008869 crossref_primary_10_1099_mgen_0_000497 crossref_primary_10_1186_s12985_015_0305_5 crossref_primary_10_1038_s41598_017_01082_y crossref_primary_10_1038_s41418_024_01278_6 crossref_primary_10_1093_nargab_lqaa070 crossref_primary_10_1186_s12864_019_5954_0 crossref_primary_10_3389_fonc_2020_01771 crossref_primary_10_3897_zookeys_1088_78990 crossref_primary_10_1093_nar_gky1172 crossref_primary_10_1186_s13059_024_03337_0 crossref_primary_10_3390_ijms161226213 crossref_primary_10_3389_fpls_2021_708286 crossref_primary_10_1002_aqc_4226 crossref_primary_10_3390_data7110167 crossref_primary_10_1038_hdy_2016_102 crossref_primary_10_14814_phy2_12942 crossref_primary_10_1007_s12230_021_09846_z crossref_primary_10_3201_eid3109_241923 crossref_primary_10_1101_gr_202200_115 crossref_primary_10_1093_zoolinnean_zlae057 crossref_primary_10_1371_journal_pone_0142951 crossref_primary_10_1016_j_plasmid_2018_04_001 crossref_primary_10_1073_pnas_2506976122 crossref_primary_10_3389_fcimb_2024_1289134 crossref_primary_10_3389_fpls_2020_00180 crossref_primary_10_1038_s41467_025_57503_4 crossref_primary_10_1186_s12885_017_3726_2 crossref_primary_10_3390_genes15050628 crossref_primary_10_1111_his_15068 crossref_primary_10_1002_jgm_3322 crossref_primary_10_1007_s00018_022_04229_x crossref_primary_10_1038_s41598_024_80142_6 crossref_primary_10_3389_fnut_2023_1287312 crossref_primary_10_1080_07060661_2023_2290041 crossref_primary_10_1016_j_jphotobiol_2025_113225 crossref_primary_10_1128_JCM_00466_19 crossref_primary_10_7554_eLife_55002 crossref_primary_10_3390_ani9080523 crossref_primary_10_1007_s00253_017_8151_6 crossref_primary_10_1089_scd_2021_0202 crossref_primary_10_1016_j_steroids_2018_09_003 crossref_primary_10_1093_treephys_tpz053 crossref_primary_10_1016_j_fm_2023_104450 crossref_primary_10_1111_1755_0998_70020 crossref_primary_10_1016_j_dib_2018_08_034 crossref_primary_10_1016_j_cropro_2023_106478 crossref_primary_10_1128_mSphere_00402_19 crossref_primary_10_1158_2159_8290_CD_19_0138 crossref_primary_10_1111_mec_15598 crossref_primary_10_1016_j_cub_2021_03_084 crossref_primary_10_1016_j_jhazmat_2016_06_055 crossref_primary_10_1128_mra_00952_22 crossref_primary_10_1016_j_ccell_2022_12_004 crossref_primary_10_1016_j_vprsr_2023_100961 crossref_primary_10_1371_journal_pcbi_1009433 crossref_primary_10_1038_s41598_019_49065_5 crossref_primary_10_1186_s12934_022_01862_w crossref_primary_10_1093_molbev_msy232 crossref_primary_10_1038_s41598_019_49219_5 crossref_primary_10_1080_03014223_2020_1766520 crossref_primary_10_1093_jhered_esab009 crossref_primary_10_3389_fevo_2022_907889 crossref_primary_10_1038_ncomms11938 crossref_primary_10_1128_mra_01235_22 crossref_primary_10_1038_s41598_022_07672_9 crossref_primary_10_1080_02648725_2023_2197717 crossref_primary_10_1186_s12879_018_3626_3 crossref_primary_10_1093_molbev_msx158 crossref_primary_10_1038_s41598_022_21836_7 crossref_primary_10_1111_acel_12999 crossref_primary_10_1093_emph_eow016 crossref_primary_10_1016_j_ympev_2023_107813 crossref_primary_10_1089_ten_tea_2020_0033 crossref_primary_10_1093_femsec_fiac084 crossref_primary_10_1016_j_jbc_2025_108327 crossref_primary_10_1038_s42003_024_07384_y crossref_primary_10_3390_toxins16100436 crossref_primary_10_3389_fmicb_2018_02559 crossref_primary_10_3390_ani15101487 crossref_primary_10_1038_s42003_020_01176_w crossref_primary_10_1002_ajb2_16365 crossref_primary_10_1038_s41388_019_0861_z crossref_primary_10_1126_science_aao2774 crossref_primary_10_1093_femsec_fiac078 crossref_primary_10_1093_femsec_fiad164 crossref_primary_10_1186_s12864_024_10040_8 crossref_primary_10_1016_j_jid_2022_07_010 crossref_primary_10_3389_fmolb_2023_1128739 crossref_primary_10_1111_1759_7714_14046 crossref_primary_10_1007_s00253_016_7588_3 crossref_primary_10_1080_23802359_2021_1942261 crossref_primary_10_1038_s41467_024_49874_x crossref_primary_10_3390_ijms22136663 crossref_primary_10_1038_s41467_021_23240_7 crossref_primary_10_3390_genes13061056 crossref_primary_10_3390_cancers15153818 crossref_primary_10_1016_j_stem_2020_09_001 crossref_primary_10_3389_fcimb_2023_1153387 crossref_primary_10_1016_j_eng_2024_04_022 crossref_primary_10_1172_JCI133934 crossref_primary_10_1002_mco2_317 crossref_primary_10_1109_TCBB_2019_2897558 crossref_primary_10_3390_ijms25137391 crossref_primary_10_1038_s41467_024_48141_3 crossref_primary_10_1002_evl3_110 crossref_primary_10_1126_scitranslmed_aan6735 crossref_primary_10_3389_fimmu_2021_651475 crossref_primary_10_1038_nmeth_4121 crossref_primary_10_1093_g3journal_jkaf067 crossref_primary_10_1101_gr_266429_120 crossref_primary_10_3389_fendo_2023_1280847 crossref_primary_10_1038_s41588_018_0046_7 crossref_primary_10_1038_s41525_021_00232_6 crossref_primary_10_1038_s41590_020_0786_2 crossref_primary_10_1016_j_sleep_2023_11_024 crossref_primary_10_1038_srep13258 crossref_primary_10_15252_embj_2022112140 crossref_primary_10_3390_agronomy12092037 crossref_primary_10_1093_femsec_fiae075 crossref_primary_10_1371_journal_pone_0244755 crossref_primary_10_3389_fpls_2023_1121811 crossref_primary_10_1128_MRA_01256_19 crossref_primary_10_1128_JB_00257_20 crossref_primary_10_1186_s12864_016_3070_y crossref_primary_10_1038_s41591_023_02631_x crossref_primary_10_3389_fonc_2020_596040 crossref_primary_10_1158_1078_0432_CCR_22_0611 crossref_primary_10_1111_mec_15301 crossref_primary_10_1371_journal_pgen_1009495 crossref_primary_10_3390_ani11030825 crossref_primary_10_1016_j_ijfoodmicro_2017_06_024 crossref_primary_10_1186_s13567_023_01240_3 crossref_primary_10_1128_mra_01168_24 crossref_primary_10_1534_g3_119_0011 crossref_primary_10_1371_journal_pone_0299891 crossref_primary_10_1016_j_biologicals_2019_03_008 crossref_primary_10_3389_fmicb_2022_812116 crossref_primary_10_1186_s13046_024_03097_6 crossref_primary_10_1017_S0031182024000568 crossref_primary_10_1038_s41598_021_94789_y crossref_primary_10_1242_bio_059189 crossref_primary_10_3390_genes13040569 crossref_primary_10_1007_s12035_025_04974_7 crossref_primary_10_1007_s10592_021_01340_7 crossref_primary_10_1016_j_bbagrm_2020_194521 crossref_primary_10_1016_j_aquatox_2019_02_009 crossref_primary_10_1126_science_abj7484 crossref_primary_10_3390_biomedicines10102412 crossref_primary_10_1186_s40168_022_01327_7 crossref_primary_10_1161_CIRCULATIONAHA_116_024545 crossref_primary_10_1007_s00262_023_03581_6 crossref_primary_10_1093_nargab_lqae036 crossref_primary_10_3389_fimmu_2024_1512578 crossref_primary_10_1186_s13068_016_0547_5 crossref_primary_10_1016_j_xpro_2024_103040 crossref_primary_10_1111_mpp_13446 crossref_primary_10_1016_j_envpol_2023_121869 crossref_primary_10_3390_ijms26030975 crossref_primary_10_3389_fmolb_2021_612881 crossref_primary_10_1128_mra_00515_24 crossref_primary_10_1186_s12864_023_09908_y crossref_primary_10_1128_mra_00687_25 crossref_primary_10_1007_s15010_024_02378_8 crossref_primary_10_1016_j_devcel_2024_05_013 crossref_primary_10_1186_s12866_020_01859_8 crossref_primary_10_15252_msb_202211301 crossref_primary_10_4049_jimmunol_1900542 crossref_primary_10_1186_s13059_020_1929_3 crossref_primary_10_1534_genetics_119_301922 crossref_primary_10_1016_j_molcel_2018_12_009 crossref_primary_10_1128_mra_00800_25 crossref_primary_10_1038_s41467_025_62568_2 crossref_primary_10_7554_eLife_92173_3 crossref_primary_10_1016_j_biocon_2025_111319 crossref_primary_10_1016_j_diagmicrobio_2024_116526 crossref_primary_10_1084_jem_20200938 crossref_primary_10_1158_2159_8290_CD_17_0891 crossref_primary_10_1038_s41598_021_82299_w crossref_primary_10_1128_MRA_00153_20 crossref_primary_10_1016_j_molp_2018_11_005 crossref_primary_10_1128_IAI_00886_16 crossref_primary_10_1038_s41598_020_77816_2 crossref_primary_10_4103_jomfp_jomfp_455_21 crossref_primary_10_1002_advs_202400969 crossref_primary_10_1016_j_cmi_2017_02_002 crossref_primary_10_1128_msphere_00393_22 crossref_primary_10_1128_aem_02420_24 crossref_primary_10_1111_pce_14258 crossref_primary_10_1038_s41416_023_02178_1 crossref_primary_10_1094_PDIS_03_17_0306_RE crossref_primary_10_1016_j_ympev_2024_108135 crossref_primary_10_1038_s41598_019_54019_y crossref_primary_10_1128_spectrum_02037_23 crossref_primary_10_1093_molbev_msaa030 crossref_primary_10_1002_ece3_6680 crossref_primary_10_1098_rsos_240132 crossref_primary_10_1186_s12864_020_07037_4 crossref_primary_10_1128_mSphere_00298_19 crossref_primary_10_1128_spectrum_03804_23 crossref_primary_10_1126_science_abb4776 crossref_primary_10_1523_ENEURO_0504_24_2025 crossref_primary_10_1128_msystems_00627_24 crossref_primary_10_1371_journal_pcbi_1004393 crossref_primary_10_1038_s43018_023_00612_0 crossref_primary_10_1038_s41417_024_00745_z crossref_primary_10_1038_s41477_024_01741_9 crossref_primary_10_1038_s41586_022_04760_8 crossref_primary_10_1016_j_csbj_2021_10_029 crossref_primary_10_1038_s41598_019_42310_x crossref_primary_10_3389_fgene_2020_560248 crossref_primary_10_1038_s41587_020_0555_7 crossref_primary_10_1002_ajmg_b_32981 crossref_primary_10_1126_science_aax0249 crossref_primary_10_1016_j_jss_2022_03_029 crossref_primary_10_1128_aac_01354_22 crossref_primary_10_1158_2159_8290_CD_23_1161 crossref_primary_10_3389_fmicb_2021_713669 crossref_primary_10_3390_v13030426 crossref_primary_10_1089_hs_2014_0076 crossref_primary_10_1186_s12859_019_2799_0 crossref_primary_10_1371_journal_pntd_0005752 crossref_primary_10_1038_s41598_021_93855_9 crossref_primary_10_1038_s41598_022_17721_y crossref_primary_10_1038_s41598_025_02014_x crossref_primary_10_1016_j_ijpara_2025_05_006 crossref_primary_10_1016_j_molcel_2023_03_026 crossref_primary_10_1007_s10682_020_10050_4 crossref_primary_10_1371_journal_pgen_1010879 crossref_primary_10_1016_j_watres_2024_122538 crossref_primary_10_7554_eLife_92173 crossref_primary_10_1007_s11046_020_00449_6 crossref_primary_10_1016_j_trsl_2022_04_004 crossref_primary_10_1038_s41586_023_05989_7 crossref_primary_10_3390_ijerph19095144 crossref_primary_10_1038_s42003_021_02818_3 crossref_primary_10_1016_j_celrep_2024_114498 crossref_primary_10_1093_plphys_kiad347 crossref_primary_10_1111_aab_12345 crossref_primary_10_1016_j_chembiol_2023_04_005 crossref_primary_10_3390_ijms232012610 crossref_primary_10_1038_s41596_022_00692_9 crossref_primary_10_1016_j_immuni_2021_03_007 crossref_primary_10_3390_plants11192614 crossref_primary_10_1128_mra_00178_25 crossref_primary_10_1016_j_celrep_2025_115639 crossref_primary_10_1128_mSphere_00532_18 crossref_primary_10_1371_journal_ppat_1009138 crossref_primary_10_1016_j_sajb_2019_02_009 crossref_primary_10_12688_wellcomeopenres_16559_1 crossref_primary_10_1007_s10722_021_01282_6 crossref_primary_10_1186_s13059_021_02265_7 crossref_primary_10_1371_journal_pgen_1010862 crossref_primary_10_3389_fenrg_2018_00030 crossref_primary_10_1038_ng_3523 crossref_primary_10_1096_fj_202301475R crossref_primary_10_1186_s12864_023_09514_y crossref_primary_10_3390_molecules23020246 crossref_primary_10_1038_s41467_024_48034_5 crossref_primary_10_1038_s41467_023_37512_x crossref_primary_10_1155_2024_2383886 crossref_primary_10_1098_rsos_160239 crossref_primary_10_1038_s41589_018_0205_2 crossref_primary_10_1016_j_molcel_2023_03_009 crossref_primary_10_1161_CIRCULATIONAHA_120_051921 crossref_primary_10_1016_j_syapm_2025_126591 crossref_primary_10_1038_srep25280 crossref_primary_10_1111_pbi_70000 crossref_primary_10_1016_j_gca_2021_10_010 crossref_primary_10_1371_journal_pone_0291072 |
| Cites_doi | 10.1016/0022-2836(81)90087-5 10.1093/nar/gkg094 10.1186/1756-0500-5-337 10.1038/nmeth.1179 10.1016/j.ygeno.2013.07.011 10.1016/0196-6774(85)90023-9 10.1093/bioinformatics/btt702 10.1186/gb-2013-14-4-r36 10.1186/gb-2009-10-5-r54 10.1016/j.ygeno.2011.05.009 10.14806/ej.17.1.200 10.1093/nar/gks540 10.1038/nmeth.2762 10.1038/nmeth.1923 10.1371/journal.pone.0085024 10.1101/gr.089532.108 10.1093/bioinformatics/btp120 10.1145/316542.316550 10.1016/0022-2836(70)90057-4 10.1093/bioinformatics/btr708 10.1186/1471-2105-11-38 10.1186/1471-2105-11-341 |
| ContentType | Journal Article |
| Copyright | Jiang et al.; licensee BioMed Central Ltd. 2014 COPYRIGHT 2014 BioMed Central Ltd. 2014 Jiang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Copyright © 2014 Jiang et al.; licensee BioMed Central Ltd. 2014 Jiang et al.; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Jiang et al.; licensee BioMed Central Ltd. 2014 – notice: COPYRIGHT 2014 BioMed Central Ltd. – notice: 2014 Jiang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. – notice: Copyright © 2014 Jiang et al.; licensee BioMed Central Ltd. 2014 Jiang et al.; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM |
| DOI | 10.1186/1471-2105-15-182 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database (ProQuest) ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Medical Database Biological Science Database ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Engineering Research Database Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 182 |
| ExternalDocumentID | PMC4074385 3354464331 A539460887 24925680 10_1186_1471_2105_15_182 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- 0R~ 23N 2WC 4.4 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D M0N P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c699t-df494052cde239a15ef7fe9d8edff62f8d733241a8beac935d6801898c3bd7703 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 1111 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000338258500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Tue Nov 04 01:59:22 EST 2025 Tue Oct 07 09:43:06 EDT 2025 Thu Sep 04 19:13:39 EDT 2025 Mon Oct 06 18:31:49 EDT 2025 Tue Nov 11 10:56:04 EST 2025 Tue Nov 04 18:15:20 EST 2025 Thu Nov 13 16:20:14 EST 2025 Thu Apr 03 07:05:02 EDT 2025 Sat Nov 29 05:39:56 EST 2025 Tue Nov 18 22:07:43 EST 2025 Sat Sep 06 07:27:14 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Approximate string matching Next generation sequencing Adapter trimming Barcode demultiplexing Local sequence alignment |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c699t-df494052cde239a15ef7fe9d8edff62f8d733241a8beac935d6801898c3bd7703 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/1541298963?pq-origsite=%requestingapplication% |
| PMID | 24925680 |
| PQID | 1541298963 |
| PQPubID | 44065 |
| PageCount | 1 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_4074385 proquest_miscellaneous_1547867061 proquest_miscellaneous_1542009074 proquest_journals_1541298963 gale_infotracmisc_A539460887 gale_infotracacademiconefile_A539460887 gale_incontextgauss_ISR_A539460887 pubmed_primary_24925680 crossref_citationtrail_10_1186_1471_2105_15_182 crossref_primary_10_1186_1471_2105_15_182 springer_journals_10_1186_1471_2105_15_182 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-06-12 |
| PublicationDateYYYYMMDD | 2014-06-12 |
| PublicationDate_xml | – month: 06 year: 2014 text: 2014-06-12 day: 12 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2014 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V |
| References | C Del Fabbro (6464_CR22) 2013; 8 R Schmieder (6464_CR8) 2010; 11 M Dodt (6464_CR9) 2012; 1 A Criscuolo (6464_CR13) 2013; 102 SB Needleman (6464_CR10) 1970; 48 M Martin (6464_CR3) 2011; 17 B Langmead (6464_CR18) 2012; 9 M Lohse (6464_CR11) 2012; 40 D Kim (6464_CR20) 2013; 14 J Falgueras (6464_CR7) 2010; 11 G Myers (6464_CR5) 1999; 46 Y Kong (6464_CR6) 2011; 98 C Trapnell (6464_CR19) 2009; 25 M Kato (6464_CR16) 2009; 10 W Huang (6464_CR15) 2012; 28 F Consortium (6464_CR21) 2003; 31 HH He (6464_CR1) 2014; 11 E Ukkonen (6464_CR4) 1985; 6 S Lindgreen (6464_CR12) 2012; 5 RM Leggett (6464_CR14) 2014; 30 TF Smith (6464_CR2) 1981; 147 LW Hillier (6464_CR17) 2008; 5 JT Simpson (6464_CR23) 2009; 19 22748135 - BMC Res Notes. 2012;5:337 19460142 - Genome Biol. 2009;10(5):R54 21651976 - Genomics. 2011 Aug;98(2):152-3 24297520 - Bioinformatics. 2014 Feb 15;30(4):566-8 19251739 - Genome Res. 2009 Jun;19(6):1117-23 7265238 - J Mol Biol. 1981 Mar 25;147(1):195-7 24376861 - PLoS One. 2013;8(12):e85024 12519974 - Nucleic Acids Res. 2003 Jan 1;31(1):172-5 23912058 - Genomics. 2013 Nov-Dec;102(5-6):500-6 24832523 - Biology (Basel). 2012 Dec 14;1(3):895-905 18204455 - Nat Methods. 2008 Feb;5(2):183-8 5420325 - J Mol Biol. 1970 Mar;48(3):443-53 20089148 - BMC Bioinformatics. 2010;11:38 22388286 - Nat Methods. 2012 Apr;9(4):357-9 19289445 - Bioinformatics. 2009 May 1;25(9):1105-11 20573248 - BMC Bioinformatics. 2010;11:341 24317252 - Nat Methods. 2014 Jan;11(1):73-8 22199392 - Bioinformatics. 2012 Feb 15;28(4):593-4 22684630 - Nucleic Acids Res. 2012 Jul;40(Web Server issue):W622-7 23618408 - Genome Biol. 2013;14(4):R36 |
| References_xml | – volume: 147 start-page: 195 issue: 1 year: 1981 ident: 6464_CR2 publication-title: J Mol Biol doi: 10.1016/0022-2836(81)90087-5 – volume: 31 start-page: 172 issue: 1 year: 2003 ident: 6464_CR21 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkg094 – volume: 5 start-page: 337 year: 2012 ident: 6464_CR12 publication-title: BMC Res Notes doi: 10.1186/1756-0500-5-337 – volume: 5 start-page: 183 issue: 2 year: 2008 ident: 6464_CR17 publication-title: Nat Methods doi: 10.1038/nmeth.1179 – volume: 102 start-page: 500 issue: 5–6 year: 2013 ident: 6464_CR13 publication-title: Genomics doi: 10.1016/j.ygeno.2013.07.011 – volume: 6 start-page: 132 issue: 1 year: 1985 ident: 6464_CR4 publication-title: J Algorithm doi: 10.1016/0196-6774(85)90023-9 – volume: 30 start-page: 566 issue: 4 year: 2014 ident: 6464_CR14 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt702 – volume: 14 start-page: 36 issue: 4 year: 2013 ident: 6464_CR20 publication-title: Genome Biol doi: 10.1186/gb-2013-14-4-r36 – volume: 10 start-page: 54 issue: 5 year: 2009 ident: 6464_CR16 publication-title: Genome Biol doi: 10.1186/gb-2009-10-5-r54 – volume: 98 start-page: 152 issue: 2 year: 2011 ident: 6464_CR6 publication-title: Genomics doi: 10.1016/j.ygeno.2011.05.009 – volume: 17 start-page: 10 year: 2011 ident: 6464_CR3 publication-title: EMBnet.journal doi: 10.14806/ej.17.1.200 – volume: 40 start-page: 622 issue: Web Server issu year: 2012 ident: 6464_CR11 publication-title: Nucleic Acids Res doi: 10.1093/nar/gks540 – volume: 11 start-page: 73 issue: 1 year: 2014 ident: 6464_CR1 publication-title: Nature Methods doi: 10.1038/nmeth.2762 – volume: 9 start-page: 357 issue: 4 year: 2012 ident: 6464_CR18 publication-title: Nat Methods doi: 10.1038/nmeth.1923 – volume: 1 start-page: 895 issue: 3 year: 2012 ident: 6464_CR9 publication-title: Biology (Basel) – volume: 8 start-page: 85024 issue: 12 year: 2013 ident: 6464_CR22 publication-title: PLoS One doi: 10.1371/journal.pone.0085024 – volume: 19 start-page: 1117 issue: 6 year: 2009 ident: 6464_CR23 publication-title: Genome Res doi: 10.1101/gr.089532.108 – volume: 25 start-page: 1105 issue: 9 year: 2009 ident: 6464_CR19 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp120 – volume: 46 start-page: 395 issue: 3 year: 1999 ident: 6464_CR5 publication-title: J ACM doi: 10.1145/316542.316550 – volume: 48 start-page: 443 issue: 3 year: 1970 ident: 6464_CR10 publication-title: J Mol Biol doi: 10.1016/0022-2836(70)90057-4 – volume: 28 start-page: 593 issue: 4 year: 2012 ident: 6464_CR15 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr708 – volume: 11 start-page: 38 year: 2010 ident: 6464_CR7 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-11-38 – volume: 11 start-page: 341 year: 2010 ident: 6464_CR8 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-11-341 – reference: 20089148 - BMC Bioinformatics. 2010;11:38 – reference: 18204455 - Nat Methods. 2008 Feb;5(2):183-8 – reference: 19251739 - Genome Res. 2009 Jun;19(6):1117-23 – reference: 24317252 - Nat Methods. 2014 Jan;11(1):73-8 – reference: 22199392 - Bioinformatics. 2012 Feb 15;28(4):593-4 – reference: 24832523 - Biology (Basel). 2012 Dec 14;1(3):895-905 – reference: 19289445 - Bioinformatics. 2009 May 1;25(9):1105-11 – reference: 22684630 - Nucleic Acids Res. 2012 Jul;40(Web Server issue):W622-7 – reference: 24297520 - Bioinformatics. 2014 Feb 15;30(4):566-8 – reference: 7265238 - J Mol Biol. 1981 Mar 25;147(1):195-7 – reference: 24376861 - PLoS One. 2013;8(12):e85024 – reference: 5420325 - J Mol Biol. 1970 Mar;48(3):443-53 – reference: 23618408 - Genome Biol. 2013;14(4):R36 – reference: 19460142 - Genome Biol. 2009;10(5):R54 – reference: 22748135 - BMC Res Notes. 2012;5:337 – reference: 22388286 - Nat Methods. 2012 Apr;9(4):357-9 – reference: 12519974 - Nucleic Acids Res. 2003 Jan 1;31(1):172-5 – reference: 20573248 - BMC Bioinformatics. 2010;11:341 – reference: 23912058 - Genomics. 2013 Nov-Dec;102(5-6):500-6 – reference: 21651976 - Genomics. 2011 Aug;98(2):152-3 |
| SSID | ssj0017805 |
| Score | 2.645686 |
| Snippet | Background
Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA... Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments.... Background Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA... Doc number: 182 Abstract Background: Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than... Background: Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA... |
| SourceID | pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 182 |
| SubjectTerms | Algorithms Analysis Animals Arabidopsis Bioinformatics Biomedical and Life Sciences Caenorhabditis elegans Computational Biology/Bioinformatics Computer Appl. in Life Sciences Deoxyribonucleic acid DNA Drosophila Dynamic programming Genetic testing Genomes High-Throughput Nucleotide Sequencing - methods Humans Life Sciences Methodology Methodology Article Methods Microarrays RNA sequencing Sequence analysis (methods) Sequence Analysis, RNA Software Time Factors |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3ri9QwEB-OU8Evvh_VU6IIohDu-srDb4d4KMghtyoHfihpHueidJdtV_G_dybbluuiBwr9lslumplMfulMfgPwTCurC3VguQ0643jesLw2Iuc1eUttESC4eFH4vTw-Vqen-sMOZMNdmJjtPoQko6eOy1qJ_RTdKMcDSslTfBS63Uu42Skq13Ay-zxGDoijfwhH_qHXZPvZdsLndqHtDMmtMGncfY6u_8-4b8C1Hmuyw41x3IQd39yCK5vqk79uw5fZN__Tr14xw4JpO2Yax4y1ayKPYMaZJU4561Zz-rTNENuyhk7JZ5GnmtTJ-jRsHAxbGnSdjnv8CUShrr0Dn47efHz9lve1FrgVWnfchUIjdsus81muTVr6IIPXTnkXgsiCcjJH7JUaVaOr1nnpBO5tClWd106i27gLu82i8feBWWkCsb4LWZRF7rPaioPMZgTeHKIFn8D-oILK9kTkVA_jexUPJEpUNGUVTVmV4qOyBF6MPZYbEo4LZJ-SVivitmgoeebMrNu2ejc7qQ7LXBeC3GoCz3uhsMC_tqa_i4AvQHRYE8m9iSQuPjttHoyn6hd_iwMpUiK2F3kCT8Zm6kkJbY1frKMMxaUQwF0oI5WQiLgSuLexx_H1ieixRBUkICeWOgoQbfi0pZl_jfThBaFGVSbwcrDXc0P_y6w--Bfhh3AVtV3wWOFpD3a71do_gsv2RzdvV4_jcv0NRiI5OQ priority: 102 providerName: Springer Nature |
| Title | Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads |
| URI | https://link.springer.com/article/10.1186/1471-2105-15-182 https://www.ncbi.nlm.nih.gov/pubmed/24925680 https://www.proquest.com/docview/1541298963 https://www.proquest.com/docview/1542009074 https://www.proquest.com/docview/1547867061 https://pubmed.ncbi.nlm.nih.gov/PMC4074385 |
| Volume | 15 |
| WOSCitedRecordID | wos000338258500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database (ProQuest) customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection (ProQuest) customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central Database Suite (ProQuest) customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3di9NAEB-8OwVf_NaLnmUVQRSWXj531xc55Q4PtYRWpepD2O5u7oqS1qZV_O-d2ab1UrAvQlgIO0mz_U1mZncnvwF4oqRRiTw03JQq4jjfMHyks5iPyFoqgwGC9R8KvxO9nhwOVd4suNVNWuXKJnpDbSeG1si76OpDYgvP4pfTH5yqRtHualNCYwf2iCUh8ql7-XoXgfj6V1uTMuuGaIg5TnFSHuIho5Yr2jTIFzzSZrbkxpap90Qn1_93DDfgWhODsqOl0tyES666BVeWVSl_34avg2_ul5u9YJqVup4zXVmmjVkQqQTTVk8RCjafjWnJm2HMyyqaPZ95_mqCmTXp2fjwbKrRpFru8BYYndr6Dnw8Of7w-g1vajBwkyk157ZMFMZ0kbEuipUOU1eK0ikrnS3LLCqlFTHGZKGWIzThKk5thj5PogrEIyvQnNyF3WpSuX1gRuiS2OAzkaRJ7KKRyQ4jE1FQZxE7F0B3BUdhGoJyqpPxvfATFZkVBGBBABYhHjIK4Nn6iumSnGOL7GNCuCDOi4qSas70oq6L00G_OEpjlWRkbgN42giVE_xpo5tvFHAARJPVkjxoSeJLadrdKw0oGqNQF3_hD-DRupuupES3yk0WXob2qzCw2yojZCYwEgvg3lI318MnAsgUIQhAtLR2LUB04u2eanzuacUTiiZlGsDzlX5fePR__Kv3t4_zAVxFfBPuaz0dwO58tnAP4bL5OR_Xsw7siKHwrezA3qvjXt7v-DURbN8K3vEvM7Z5-gX789P3-Wc86w8-_QEO_E41 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aAwQv3C-BAQaBEEhWFydxbCSEJmBa1VIhNqRJPATXdkYFSkvTMu1P8Rs5J03KUom-7QEpbz5Je-xz-Xz7DsBTrayO1bblNteC43zD8qGRER9StNQWAYKrLgr308FAHR7qjxvwu7kLQ8cqm5hYBWo3trRG3sFUHxJbuIzeTH5yqhpFu6tNCY2FWfT8yTFO2crX3Xc4vs-E2H1_8HaP11UFuJVaz7jLY40oRVjnRaRNmPg8zb12yrs8lyJXLo0QZYRGDTEo6ShxEqO4QqWioUvRQfC75-B8HKmU_KqX8uWuBdUHaLZCleyEGPg5TqkSHuKjRCv1rSaAUxlw9XTmyhZtlfl2r_5vfXYNrtQYm-0snOI6bPjiBlxcVN08uQlf9r_7Yz99xQzLTTljpnDMWDsn0gxmnJmgqbHZdERL-gwxPStodeCo4ucmM2b18XPsLDYxmDIc9_gJRN-uvAWfz0Sz27BZjAt_F5hNTU5s9zKNkzjyYmjltrCCQKtDlOQD6DTDn9magJ3qgPzIqomYkhkZTEYGk4X4KBHAi-UbkwX5yBrZJ2RRGXF6FHRo6MjMyzLr7n_KdpJIx5LSSQDPa6F8jD9tTX0HAxUgGrCW5FZLEoOObTc3FpfVQa_M_ppbAI-XzfQmHeQr_HheydB-HALXtTKpkikizQDuLHxhqT4RXCY4BAGkLS9ZChBderulGH2raNNjQssqCeBl40-n_vo_evXeej0fwaW9gw_9rN8d9O7DZRzrmFd1rbZgczad-wdwwf6ajcrpwypQMPh61l72BxUlozQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3ri9QwEB_kfOAX34_qqVEE8SDs9ZUmfjvUxcNjOVyVAz-UNI9zUbrLtqv43zvTF9dFD0Tot0zaJDOZ_KaZ_ALwXEmjErlvuPEq4hhvGF5oEfOCvKUyCBBsc1D4KJvN5MmJOu5-uFV9tnu_JdmeaSCWprKerKxvp7gUkxBdKsdgJeUhPhJd8MWE0ugpWp9_HnYRiK-_35r8Q63RUrTtkM-sSNvZkltbps1KNL3-v324Adc6DMoOWqO5CRdceQsut7dS_roNX-bf3E-3fsU087qqmS4t08ZsiFSCaatXqApWrxf0y5sh5mUlRc-nDX81qZl16dnYMLbS6FItd_gKRKe2ugOfpm8_vn7HuzsYuBFK1dz6RCGmi4x1Uax0mDqfeaesdNZ7EXlpsxgxWahlgS5cxakVuOZJNIG4sBm6k7uwUy5Ldx-YybQnNniRJWkSu6gwYj8yEYE6iyjCBTDp1ZGbjqCc7sn4njeBihQ5DVlOQ5aH-MgogJdDjVVLznGO7DPScE6cFyUl1ZzqTVXlh_MP-UEaq0SQuw3gRSfkl_hpo7szCtgBoskaSe6OJHFSmnFxb0h55xQqbEgSEuG9iAN4OhRTTUp0K91y08jQfhUCu3NlMikyRGIB3Gttc-g-EUCmqIIAspHVDgJEJz4uKRdfG1rxhNCkTAPY6233TNP_MqoP_kX4CVw5fjPNjw5n7x_CVVR8wptLoHZhp15v3CO4ZH7Ui2r9uJnFvwHtekUB |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Skewer%3A+a+fast+and+accurate+adapter+trimmer+for+next-generation+sequencing+paired-end+reads&rft.jtitle=BMC+bioinformatics&rft.au=Jiang%2C+Hongshan&rft.au=Lei%2C+Rong&rft.au=Ding%2C+Shou-Wei&rft.au=Zhu%2C+Shuifang&rft.date=2014-06-12&rft.pub=Springer+Nature+B.V&rft.eissn=1471-2105&rft.volume=15&rft_id=info:doi/10.1186%2F1471-2105-15-182&rft.externalDocID=3354464331 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |