MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a co...

Full description

Saved in:

Bibliographic Details
Published in:	BMC bioinformatics Vol. 15; no. 1; p. 224
Main Authors:	Kalari, Krishna R, Nair, Asha A, Bhavsar, Jaysheel D, O’Brien, Daniel R, Davila, Jaime I, Bockol, Matthew A, Nie, Jinfu, Tang, Xiaojia, Baheti, Saurabh, Doughty, Jay B, Middha, Sumit, Sicotte, Hugues, Thompson, Aubrey E, Asmann, Yan W, Kocher, Jean-Pierre A
Format:	Journal Article
Language:	English
Published:	London BioMed Central 27.06.2014 BioMed Central Ltd Springer Nature B.V
Subjects:	Algorithms Analysis Base Sequence Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Exons - genetics Gene Expression Profiling Genes Genomics - methods Health Facilities High-Throughput Nucleotide Sequencing - methods Humans Life Sciences Microarrays Research projects RNA sequencing Sequence Analysis, RNA - methods Software Transcriptome analysis Transcriptomic sequencing Fusion transcripts RNA-Seq reports RNA-Seq Bioinformatics workflow Exon counts Gene expression Expressed single nucleotide variants
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/ .
AbstractList	Doc number: 224 Abstract Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/ . Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from Keywords: Transcriptomic sequencing, RNA-Seq, Bioinformatics workflow, Gene expression, Exon counts, Fusion transcripts, Expressed single nucleotide variants, RNA-Seq reports Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/ . Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.BACKGROUNDAlthough the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients.RESULTSFor optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients.Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.CONCLUSIONSOur software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/. Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/ . Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/. Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.
ArticleNumber	224
Audience	Academic
Author	Bhavsar, Jaysheel D Doughty, Jay B Davila, Jaime I O’Brien, Daniel R Kalari, Krishna R Bockol, Matthew A Tang, Xiaojia Middha, Sumit Sicotte, Hugues Nair, Asha A Baheti, Saurabh Asmann, Yan W Kocher, Jean-Pierre A Nie, Jinfu Thompson, Aubrey E
AuthorAffiliation	1 Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA 4 Present Address: Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA 2 Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA 3 Department of Health Sciences Research, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
AuthorAffiliation_xml	– name: 3 Department of Health Sciences Research, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA – name: 1 Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA – name: 2 Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA – name: 4 Present Address: Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
Author_xml	– sequence: 1 givenname: Krishna R surname: Kalari fullname: Kalari, Krishna R organization: Department of Health Sciences Research, Mayo Clinic – sequence: 2 givenname: Asha A surname: Nair fullname: Nair, Asha A organization: Department of Health Sciences Research, Mayo Clinic – sequence: 3 givenname: Jaysheel D surname: Bhavsar fullname: Bhavsar, Jaysheel D organization: Department of Health Sciences Research, Mayo Clinic – sequence: 4 givenname: Daniel R surname: O’Brien fullname: O’Brien, Daniel R organization: Department of Health Sciences Research, Mayo Clinic – sequence: 5 givenname: Jaime I surname: Davila fullname: Davila, Jaime I organization: Department of Health Sciences Research, Mayo Clinic – sequence: 6 givenname: Matthew A surname: Bockol fullname: Bockol, Matthew A organization: Department of Health Sciences Research, Mayo Clinic – sequence: 7 givenname: Jinfu surname: Nie fullname: Nie, Jinfu organization: Department of Health Sciences Research, Mayo Clinic – sequence: 8 givenname: Xiaojia surname: Tang fullname: Tang, Xiaojia organization: Department of Health Sciences Research, Mayo Clinic – sequence: 9 givenname: Saurabh surname: Baheti fullname: Baheti, Saurabh organization: Department of Health Sciences Research, Mayo Clinic – sequence: 10 givenname: Jay B surname: Doughty fullname: Doughty, Jay B organization: Department of Health Sciences Research, Mayo Clinic – sequence: 11 givenname: Sumit surname: Middha fullname: Middha, Sumit organization: Department of Health Sciences Research, Mayo Clinic – sequence: 12 givenname: Hugues surname: Sicotte fullname: Sicotte, Hugues organization: Department of Health Sciences Research, Mayo Clinic – sequence: 13 givenname: Aubrey E surname: Thompson fullname: Thompson, Aubrey E organization: Department of Cancer Biology, Mayo Clinic – sequence: 14 givenname: Yan W surname: Asmann fullname: Asmann, Yan W organization: Department of Health Sciences Research, Mayo Clinic – sequence: 15 givenname: Jean-Pierre A surname: Kocher fullname: Kocher, Jean-Pierre A email: kocher.jeanpierre@mayo.edu organization: Department of Health Sciences Research, Mayo Clinic, Department of Health Sciences Research, Mayo Clinic
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24972667$$D View this record in MEDLINE/PubMed
BookMark	eNqNkk1v1DAQhi1URD_gzglF4kIPKR7HdhIOSFFVoFJLqy2cLdeZBFdZexsnVfff47Bl2a2gQj7YGj_vfO-THecdEvIa6BFAId8DzyFlQEUKImWMPyN7a9POxnuX7IdwQynkBRUvyC7jZc6kzPdIdl5dprMrvP2QnOulTyqnu2WwIbm0C-ysw6TxfTL7WiUBb0d0xrr2JXne6C7gq4f7gHz_dPLt-Et6dvH59Lg6S42kdEh1JqHQQgqMgSHjCNTwEoDVyDKstWiyQhswZV7nJa2BymuDBZOmKWta8Cw7IB9Xfhfj9Rxrg27odacWvZ3rfqm8tmr7x9kfqvV3ijNWCArRwbsHB72PyYdBzW0w2HXaoR-DAsHzQpZQsP9Bec5jGXlE3z5Cb_zYx76tKJ4JUfI_VKs7VNY1PqZoJqeqElnJJaO_Mjz6CxVPjXNr4rAbG-1bgsMtQWQGvB9aPYagTq9m2-ybzf6tG_d7-hGgK8D0PoQemzUCVE0LpqYNUtMGxdJUXLAokY8kxg56sH6agO2eEsJKGGIM12K_0bV_aX4CdLbccA
CitedBy_id	crossref_primary_10_3390_ijms252212130 crossref_primary_10_1097_CMR_0000000000000921 crossref_primary_10_1038_s41598_020_76881_x crossref_primary_10_1080_15592294_2020_1819663 crossref_primary_10_1093_ecco_jcc_jjad093 crossref_primary_10_1016_j_cgh_2024_06_028 crossref_primary_10_1681_ASN_2019020150 crossref_primary_10_1016_j_jid_2025_01_015 crossref_primary_10_1016_j_ebiom_2022_103929 crossref_primary_10_1038_s41551_019_0460_x crossref_primary_10_1007_s10875_018_0499_6 crossref_primary_10_1186_s13059_021_02498_6 crossref_primary_10_1016_j_euf_2016_03_008 crossref_primary_10_1016_j_gene_2018_05_034 crossref_primary_10_1126_science_abj2890 crossref_primary_10_1186_s12859_015_0873_9 crossref_primary_10_1007_s00401_024_02720_2 crossref_primary_10_1158_0008_5472_CAN_16_3011 crossref_primary_10_1002_jcp_26211 crossref_primary_10_1002_rco2_14 crossref_primary_10_1186_s12974_021_02360_3 crossref_primary_10_1002_jcp_30933 crossref_primary_10_1038_s41467_019_09964_7 crossref_primary_10_1186_s12859_019_3219_1 crossref_primary_10_1016_j_yexcr_2021_112779 crossref_primary_10_1111_acel_13606 crossref_primary_10_1111_his_14961 crossref_primary_10_1016_j_modpat_2023_100246 crossref_primary_10_1186_s12859_017_1770_1 crossref_primary_10_1038_s41598_024_75250_2 crossref_primary_10_1186_s13098_018_0359_9 crossref_primary_10_1371_journal_pone_0200005 crossref_primary_10_1002_lary_30109 crossref_primary_10_1016_j_scr_2019_101423 crossref_primary_10_1038_srep25749 crossref_primary_10_1038_s41380_023_02372_w crossref_primary_10_1038_s41379_021_00843_5 crossref_primary_10_1002_jcp_29927 crossref_primary_10_1038_s41467_022_31890_4 crossref_primary_10_1016_j_jpi_2025_100443 crossref_primary_10_1016_j_mimet_2018_10_008 crossref_primary_10_1186_s13287_023_03269_9 crossref_primary_10_1038_s41593_022_01127_0 crossref_primary_10_1002_1878_0261_12414 crossref_primary_10_1089_scd_2019_0030 crossref_primary_10_1016_j_gene_2014_08_041 crossref_primary_10_1136_gutjnl_2022_327471 crossref_primary_10_3389_falgy_2025_1606255 crossref_primary_10_1093_bib_bbv036 crossref_primary_10_1016_j_cell_2020_08_007 crossref_primary_10_2106_JBJS_22_00605 crossref_primary_10_1111_his_13649 crossref_primary_10_1038_s41431_018_0222_3 crossref_primary_10_1186_s13287_023_03372_x crossref_primary_10_3389_fgene_2024_1412767 crossref_primary_10_1093_nargab_lqaf053 crossref_primary_10_1096_fj_202001192R crossref_primary_10_3390_cells10040763 crossref_primary_10_1016_j_omtn_2023_05_026 crossref_primary_10_1681_ASN_0000000000000440 crossref_primary_10_3389_fcell_2021_681153 crossref_primary_10_1093_brain_awaa141 crossref_primary_10_1016_j_gene_2022_146400 crossref_primary_10_1093_bib_bbae164 crossref_primary_10_1186_s12859_021_04211_7 crossref_primary_10_1186_s12920_018_0391_5 crossref_primary_10_1016_j_cels_2016_10_018 crossref_primary_10_1038_s41380_022_01769_3 crossref_primary_10_2217_epi_2023_0026 crossref_primary_10_1016_j_jacep_2025_03_039 crossref_primary_10_1016_j_genrep_2019_100363 crossref_primary_10_1371_journal_pone_0145176 crossref_primary_10_1038_s41434_022_00369_8 crossref_primary_10_1002_jbmr_3975 crossref_primary_10_1038_s41467_020_19264_0 crossref_primary_10_1186_s12859_019_3251_1 crossref_primary_10_3389_fgene_2022_836841 crossref_primary_10_1038_s43856_024_00728_z crossref_primary_10_3389_fgene_2016_00080 crossref_primary_10_1038_s41598_022_24550_6 crossref_primary_10_1002_jor_23834 crossref_primary_10_1002_hep4_1311 crossref_primary_10_1002_hep4_1315 crossref_primary_10_1186_s13024_023_00599_3 crossref_primary_10_1523_JNEUROSCI_2264_20_2021 crossref_primary_10_1016_j_humpath_2023_11_008 crossref_primary_10_1016_j_ygeno_2018_09_013 crossref_primary_10_1097_AS9_0000000000000444 crossref_primary_10_1016_j_cgh_2020_02_027 crossref_primary_10_1016_j_humpath_2019_03_007 crossref_primary_10_1016_j_ygyno_2019_06_010 crossref_primary_10_1016_j_nbd_2021_105294 crossref_primary_10_1002_anbr_202100127 crossref_primary_10_1212_NXG_0000000000000126 crossref_primary_10_1128_spectrum_04307_23 crossref_primary_10_1038_s41467_022_30056_6 crossref_primary_10_1016_j_eururo_2025_03_012 crossref_primary_10_3390_cancers15020477 crossref_primary_10_1016_j_gene_2020_144634 crossref_primary_10_1093_neuonc_noz129 crossref_primary_10_1016_j_bbrc_2017_03_033 crossref_primary_10_1038_s41467_023_42626_3 crossref_primary_10_1093_ajcp_aqac125 crossref_primary_10_1084_jem_20180484 crossref_primary_10_1167_iovs_18_25067 crossref_primary_10_3390_genes16080988 crossref_primary_10_1136_jitc_2022_006035 crossref_primary_10_1038_s41591_018_0071_1 crossref_primary_10_1158_1078_0432_CCR_22_2215 crossref_primary_10_1002_hep_27687 crossref_primary_10_1186_s12864_016_3329_3 crossref_primary_10_1186_s12885_018_4594_0 crossref_primary_10_1038_s41467_024_48926_6 crossref_primary_10_1038_s41588_021_00886_z crossref_primary_10_1038_s41540_022_00235_8 crossref_primary_10_1111_jnc_70047 crossref_primary_10_1371_journal_pone_0315285 crossref_primary_10_3389_fimmu_2024_1358477 crossref_primary_10_3390_app13042698 crossref_primary_10_1186_s13287_021_02241_9 crossref_primary_10_1186_s13287_024_03847_5 crossref_primary_10_1177_0963689718780942 crossref_primary_10_1186_s40169_016_0133_2 crossref_primary_10_1016_j_jid_2022_01_015 crossref_primary_10_1002_gcc_22742 crossref_primary_10_3390_ncrna8010015 crossref_primary_10_1016_j_modpat_2024_100598 crossref_primary_10_1186_s13059_021_02418_8 crossref_primary_10_1158_1541_7786_MCR_17_0063 crossref_primary_10_1016_j_jmoldx_2018_03_007 crossref_primary_10_1186_1471_2164_16_S6_S3 crossref_primary_10_1002_gcc_23045 crossref_primary_10_1038_s41408_019_0173_0 crossref_primary_10_1038_s41467_019_14003_6 crossref_primary_10_1038_s41564_022_01121_z crossref_primary_10_3390_genes13020241 crossref_primary_10_1016_j_matbio_2020_02_004 crossref_primary_10_1016_j_ymeth_2015_04_018 crossref_primary_10_1074_jbc_M115_684399 crossref_primary_10_1186_s13000_019_0809_1 crossref_primary_10_1096_fj_201901719RR crossref_primary_10_1038_s41408_019_0233_5 crossref_primary_10_1111_acel_12352 crossref_primary_10_1158_1541_7786_MCR_19_1026 crossref_primary_10_1038_s41467_019_12610_x crossref_primary_10_1038_s41408_018_0160_x crossref_primary_10_1167_iovs_19_27689 crossref_primary_10_1158_2326_6066_CIR_21_0539 crossref_primary_10_1007_s00395_022_00932_9 crossref_primary_10_1016_j_bbadis_2019_165630 crossref_primary_10_1177_09636897241290367 crossref_primary_10_1186_s13287_024_03778_1 crossref_primary_10_1038_s41467_021_23186_w crossref_primary_10_3389_fbioe_2022_903907 crossref_primary_10_1038_s42003_019_0460_0 crossref_primary_10_1042_CS20230555 crossref_primary_10_1186_s12864_016_3161_9 crossref_primary_10_1186_s13024_018_0289_x crossref_primary_10_1016_j_gene_2020_144437 crossref_primary_10_1161_CIRCGEN_122_003756 crossref_primary_10_1186_s12920_018_0379_1 crossref_primary_10_1016_j_ejca_2024_113914 crossref_primary_10_1007_s00401_021_02361_9 crossref_primary_10_1016_j_jcjp_2021_100003 crossref_primary_10_1002_jcp_31168 crossref_primary_10_1016_j_cmet_2023_10_010 crossref_primary_10_1016_j_ajhg_2015_04_015 crossref_primary_10_1038_s41467_018_04383_6 crossref_primary_10_1038_nn_4065 crossref_primary_10_1371_journal_pone_0214588 crossref_primary_10_1016_j_ygeno_2020_03_004 crossref_primary_10_1093_function_zqac034 crossref_primary_10_1038_labinvest_2015_51 crossref_primary_10_1016_j_gene_2022_146928 crossref_primary_10_1371_journal_pcbi_1004393 crossref_primary_10_1016_j_scr_2020_101877 crossref_primary_10_3390_ijms22126580 crossref_primary_10_1038_s41467_021_22399_3 crossref_primary_10_1016_j_mayocp_2022_03_034 crossref_primary_10_1002_adbi_202000119 crossref_primary_10_1186_s12859_016_0923_y crossref_primary_10_1016_j_heliyon_2024_e39607 crossref_primary_10_1097_HC9_0000000000000613 crossref_primary_10_1002_jcp_29415 crossref_primary_10_1016_j_bbrc_2018_01_092 crossref_primary_10_1109_TNB_2018_2851997 crossref_primary_10_1016_j_jid_2023_11_027 crossref_primary_10_3389_fncel_2024_1368018 crossref_primary_10_3390_v14122763 crossref_primary_10_1093_bib_bbz110 crossref_primary_10_1002_alz_70112 crossref_primary_10_1093_ajcp_aqaa136 crossref_primary_10_1016_j_genrep_2024_101989 crossref_primary_10_1093_pnasnexus_pgae096 crossref_primary_10_1002_eji_202049054 crossref_primary_10_1063_1_5064596 crossref_primary_10_1002_jbmr_4537 crossref_primary_10_1371_journal_pone_0251732 crossref_primary_10_1186_s12859_019_3015_y crossref_primary_10_1371_journal_pone_0308711 crossref_primary_10_1007_s00401_023_02655_0 crossref_primary_10_1016_j_jmoldx_2025_07_008 crossref_primary_10_1093_jnen_nlab075 crossref_primary_10_1242_jcs_233486 crossref_primary_10_3390_ijms26136182 crossref_primary_10_1038_s41598_024_56920_7 crossref_primary_10_1111_apt_16591 crossref_primary_10_1002_bit_28356 crossref_primary_10_1016_j_neuron_2020_02_034 crossref_primary_10_1158_1078_0432_CCR_20_1232 crossref_primary_10_1186_s12920_022_01355_0 crossref_primary_10_1038_s41408_024_01207_3 crossref_primary_10_3389_fnins_2016_00071 crossref_primary_10_1016_j_humpath_2023_07_004 crossref_primary_10_1053_j_gastro_2022_10_011 crossref_primary_10_1038_s41467_022_29142_6 crossref_primary_10_1177_00220345221132268 crossref_primary_10_1210_clinem_dgac038 crossref_primary_10_1155_2020_8845635 crossref_primary_10_1002_lary_30544 crossref_primary_10_1016_j_jtho_2021_10_022 crossref_primary_10_3390_cells12101442 crossref_primary_10_1038_s41408_020_0320_7 crossref_primary_10_1186_s12864_024_10240_2 crossref_primary_10_1093_ajcp_aqaa115 crossref_primary_10_2337_db19_1268 crossref_primary_10_1186_s12920_021_01017_7 crossref_primary_10_1016_j_ajhg_2019_05_011 crossref_primary_10_1093_ecco_jcc_jjz109 crossref_primary_10_1038_s41523_022_00387_0 crossref_primary_10_1371_journal_pone_0266454 crossref_primary_10_1126_scitranslmed_aay1809 crossref_primary_10_1016_j_bbadis_2023_166784 crossref_primary_10_1016_j_jmoldx_2021_01_008 crossref_primary_10_1126_science_aaf7463 crossref_primary_10_1002_hep4_1861 crossref_primary_10_1371_journal_pone_0174303 crossref_primary_10_7554_eLife_43821 crossref_primary_10_1002_jbmr_3236 crossref_primary_10_1002_2211_5463_13901 crossref_primary_10_1177_1176935118755341 crossref_primary_10_1016_j_trsl_2023_12_004 crossref_primary_10_1038_nrg_2016_10 crossref_primary_10_1158_1078_0432_CCR_21_0343 crossref_primary_10_1038_s41467_025_60049_0 crossref_primary_10_3390_ijms20205069 crossref_primary_10_1002_ana_26537 crossref_primary_10_1016_j_trsl_2017_06_013 crossref_primary_10_4049_jimmunol_1900454 crossref_primary_10_1242_bio_060542 crossref_primary_10_1038_ncomms9653 crossref_primary_10_1093_neuonc_noac051 crossref_primary_10_1097_PAS_0000000000001574 crossref_primary_10_1038_s41467_022_32576_7 crossref_primary_10_1080_10428194_2018_1498492 crossref_primary_10_3389_fimmu_2023_1168784
Cites_doi	10.1093/bioinformatics/btt656 10.1186/gb-2013-14-9-r95 10.1371/journal.pone.0081925 10.1093/bioinformatics/btq033 10.1093/bioinformatics/btq643 10.1371/journal.pone.0058714 10.1101/gr.107524.110 10.1093/bioinformatics/btr441 10.1186/1471-2105-11-422 10.1186/gb-2011-12-8-r72 10.2202/1544-6115.1027 10.1093/bioinformatics/btp352 10.1093/bioinformatics/bts356 10.1093/bioinformatics/btp120 10.1186/gb-2009-10-3-r25 10.1371/journal.pone.0087113 10.1093/bioinformatics/btp615 10.1101/gr.092759.109 10.1093/nar/gkq622 10.1007/978-1-62703-493-7_25 10.1093/bioinformatics/btp616 10.1186/gb-2010-11-10-r106 10.1093/bioinformatics/btr427 10.1101/gr.229202. Article published online before March 2002 10.1093/bioinformatics/btp486 10.1186/1471-2105-14-91 10.1093/bioinformatics/btr012
ContentType	Journal Article
Copyright	Kalari et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. COPYRIGHT 2014 BioMed Central Ltd. 2014 Kalari 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 Kalari et al.; licensee BioMed Central Ltd. 2014 Kalari et al.; licensee BioMed Central Ltd.
Copyright_xml	– notice: Kalari et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. – notice: COPYRIGHT 2014 BioMed Central Ltd. – notice: 2014 Kalari 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 Kalari et al.; licensee BioMed Central Ltd. 2014 Kalari 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 7TM 5PM
DOI	10.1186/1471-2105-15-224
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 Edition) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Advanced Technologies & Aerospace Database 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 Nucleic Acids Abstracts 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 Nucleic Acids Abstracts
DatabaseTitleList	Publicly Available Content Database Engineering Research Database MEDLINE - Academic 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	224
ExternalDocumentID	PMC4228501 3369317531 A539462001 24972667 10_1186_1471_2105_15_224
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 7TM 5PM
ID	FETCH-LOGICAL-c600t-a3618a565e001134e10c49112de23eda5f38ac1c97d790d106bce826cf9d08433
IEDL.DBID	K7-
ISICitedReferencesCount	297
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000338733500002&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 01:56:01 EST 2025 Tue Oct 07 09:25:07 EDT 2025 Thu Sep 04 19:33:10 EDT 2025 Mon Oct 06 18:24:02 EDT 2025 Tue Nov 11 10:56:05 EST 2025 Tue Nov 04 18:15:21 EST 2025 Thu Nov 13 16:20:14 EST 2025 Mon Jul 21 05:59:52 EDT 2025 Tue Nov 18 22:07:43 EST 2025 Sat Nov 29 05:39:56 EST 2025 Sat Sep 06 07:27:14 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Transcriptomic sequencing Fusion transcripts RNA-Seq reports RNA-Seq Bioinformatics workflow Exon counts Gene expression Expressed single nucleotide variants
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-c600t-a3618a565e001134e10c49112de23eda5f38ac1c97d790d106bce826cf9d08433
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/1544435594?pq-origsite=%requestingapplication%
PMID	24972667
PQID	1544435594
PQPubID	44065
PageCount	1
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_4228501 proquest_miscellaneous_1547869182 proquest_miscellaneous_1544741137 proquest_journals_1544435594 gale_infotracmisc_A539462001 gale_infotracacademiconefile_A539462001 gale_incontextgauss_ISR_A539462001 pubmed_primary_24972667 crossref_primary_10_1186_1471_2105_15_224 crossref_citationtrail_10_1186_1471_2105_15_224 springer_journals_10_1186_1471_2105_15_224
PublicationCentury	2000
PublicationDate	2014-06-27
PublicationDateYYYYMMDD	2014-06-27
PublicationDate_xml	– month: 06 year: 2014 text: 2014-06-27 day: 27
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	Y Wang (6491_CR9) 2011; 27 YH Chen (6491_CR2) 2009; 25 A Goncalves (6491_CR6) 2011; 27 GK Smyth (6491_CR32) 2004; 3 C Soneson (6491_CR33) 2013; 14 F Seyednasrollah (6491_CR34) 2013 SR Head (6491_CR3) 2013; 1034 A McKenna (6491_CR18) 2010; 20 F Rapaport (6491_CR35) 2013; 14 6491_CR20 D Kim (6491_CR36) 2011; 12 L Habegger (6491_CR7) 2011; 27 6491_CR28 T Sakuma (6491_CR26) 2014 CL Barrett (6491_CR1) 2013; 8 GR Grant (6491_CR29) 2011; 27 S Anders (6491_CR31) 2010; 11 TJ Hardcastle (6491_CR30) 2010; 11 N Norton (6491_CR25) 2013; 8 WJ Kent (6491_CR14) 2002; 12 LG Wang (6491_CR21) 2012; 28 B Langmead (6491_CR15) 2009; 10 C Trapnell (6491_CR23) 2009; 25 6491_CR10 M Krzywinski (6491_CR16) 2009; 19 K Wang (6491_CR5) 2010; 38 MD Robinson (6491_CR4) 2010; 26 S Anders (6491_CR19) 2014 6491_CR17 Y Liao (6491_CR27) 2014; 30 JB Egan (6491_CR24) 2014; 9 J Qi (6491_CR8) 2010; 26 6491_CR12 6491_CR11 AR Quinlan (6491_CR13) 2010; 26 H Li (6491_CR22) 2009; 25 20979621 - Genome Biol. 2010;11(10):R106 23527012 - PLoS One. 2013;8(3):e58714 16646809 - Stat Appl Genet Mol Biol. 2004;3:Article3 20802226 - Nucleic Acids Res. 2010 Oct;38(18):e178 24478440 - J Virol. 2014 Apr;88(8):4069-82 20644199 - Genome Res. 2010 Sep;20(9):1297-303 21835007 - Genome Biol. 2011;12(8):R72 19880367 - Bioinformatics. 2010 Jan 1;26(1):127-9 24300110 - Brief Bioinform. 2015 Jan;16(1):59-70 22743226 - Bioinformatics. 2012 Aug 15;28(16):2184-5 19289445 - Bioinformatics. 2009 May 1;25(9):1105-11 21795323 - Bioinformatics. 2011 Sep 15;27(18):2598-600 19541911 - Genome Res. 2009 Sep;19(9):1639-45 24278466 - PLoS One. 2013;8(11):e81925 24505276 - PLoS One. 2014;9(2):e87113 20698981 - BMC Bioinformatics. 2010;11:422 24227677 - Bioinformatics. 2014 Apr 1;30(7):923-30 19910308 - Bioinformatics. 2010 Jan 1;26(1):139-40 19261174 - Genome Biol. 2009;10(3):R25 19675096 - Bioinformatics. 2009 Oct 1;25(19):2514-21 23497356 - BMC Bioinformatics. 2013;14:91 25260700 - Bioinformatics. 2015 Jan 15;31(2):166-9 21134889 - Bioinformatics. 2011 Jan 15;27(2):281-3 19505943 - Bioinformatics. 2009 Aug 15;25(16):2078-9 20110278 - Bioinformatics. 2010 Mar 15;26(6):841-2 23775753 - Methods Mol Biol. 2013;1034:385-403 21233166 - Bioinformatics. 2011 Mar 15;27(6):867-9 24020486 - Genome Biol. 2013;14(9):R95 11932250 - Genome Res. 2002 Apr;12(4):656-64 21775302 - Bioinformatics. 2011 Sep 15;27(18):2518-28
References_xml	– volume: 30 start-page: 923 issue: 7 year: 2014 ident: 6491_CR27 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt656 – volume: 14 start-page: R95 issue: 9 year: 2013 ident: 6491_CR35 publication-title: Genome Biol doi: 10.1186/gb-2013-14-9-r95 – ident: 6491_CR20 – volume: 8 start-page: e81925 issue: 11 year: 2013 ident: 6491_CR25 publication-title: PLoS One doi: 10.1371/journal.pone.0081925 – volume: 26 start-page: 841 issue: 6 year: 2010 ident: 6491_CR13 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq033 – ident: 6491_CR28 – volume: 27 start-page: 281 issue: 2 year: 2011 ident: 6491_CR7 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq643 – volume: 8 start-page: e58714 issue: 3 year: 2013 ident: 6491_CR1 publication-title: PLoS One doi: 10.1371/journal.pone.0058714 – volume: 20 start-page: 1297 issue: 9 year: 2010 ident: 6491_CR18 publication-title: Genome Res doi: 10.1101/gr.107524.110 – volume: 27 start-page: 2598 issue: 18 year: 2011 ident: 6491_CR9 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr441 – volume: 11 start-page: 422 year: 2010 ident: 6491_CR30 publication-title: BMC Bioinform doi: 10.1186/1471-2105-11-422 – volume: 12 start-page: 1 issue: 8 year: 2011 ident: 6491_CR36 publication-title: Genome Biol doi: 10.1186/gb-2011-12-8-r72 – ident: 6491_CR12 – ident: 6491_CR10 – volume: 3 start-page: Article 3 year: 2004 ident: 6491_CR32 publication-title: Stat Appl Genet Mol Biol doi: 10.2202/1544-6115.1027 – volume: 25 start-page: 2078 issue: 16 year: 2009 ident: 6491_CR22 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp352 – volume-title: bioRxiv preprintbioRxiv preprint year: 2014 ident: 6491_CR19 – volume: 28 start-page: 2184 issue: 16 year: 2012 ident: 6491_CR21 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts356 – volume: 25 start-page: 1105 issue: 9 year: 2009 ident: 6491_CR23 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp120 – volume: 10 start-page: R25 issue: 3 year: 2009 ident: 6491_CR15 publication-title: Genome Biol doi: 10.1186/gb-2009-10-3-r25 – volume: 9 start-page: e87113 issue: 2 year: 2014 ident: 6491_CR24 publication-title: PLoS One doi: 10.1371/journal.pone.0087113 – ident: 6491_CR17 – volume: 26 start-page: 127 issue: 1 year: 2010 ident: 6491_CR8 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp615 – volume-title: Brief Bioinform year: 2013 ident: 6491_CR34 – volume: 19 start-page: 1639 issue: 9 year: 2009 ident: 6491_CR16 publication-title: Genome Res doi: 10.1101/gr.092759.109 – volume: 38 start-page: e178 issue: 18 year: 2010 ident: 6491_CR5 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkq622 – volume-title: J Virol year: 2014 ident: 6491_CR26 – ident: 6491_CR11 – volume: 1034 start-page: 385 year: 2013 ident: 6491_CR3 publication-title: Methods Mol Biol doi: 10.1007/978-1-62703-493-7_25 – volume: 26 start-page: 139 issue: 1 year: 2010 ident: 6491_CR4 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp616 – volume: 11 start-page: R106 issue: 10 year: 2010 ident: 6491_CR31 publication-title: Genome Biol doi: 10.1186/gb-2010-11-10-r106 – volume: 27 start-page: 2518 issue: 18 year: 2011 ident: 6491_CR29 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr427 – volume: 12 start-page: 656 issue: 4 year: 2002 ident: 6491_CR14 publication-title: Genome Res doi: 10.1101/gr.229202. Article published online before March 2002 – volume: 25 start-page: 2514 issue: 19 year: 2009 ident: 6491_CR2 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp486 – volume: 14 start-page: 91 year: 2013 ident: 6491_CR33 publication-title: BMC Bioinform doi: 10.1186/1471-2105-14-91 – volume: 27 start-page: 867 issue: 6 year: 2011 ident: 6491_CR6 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr012 – reference: 21233166 - Bioinformatics. 2011 Mar 15;27(6):867-9 – reference: 24478440 - J Virol. 2014 Apr;88(8):4069-82 – reference: 21795323 - Bioinformatics. 2011 Sep 15;27(18):2598-600 – reference: 21775302 - Bioinformatics. 2011 Sep 15;27(18):2518-28 – reference: 20644199 - Genome Res. 2010 Sep;20(9):1297-303 – reference: 19505943 - Bioinformatics. 2009 Aug 15;25(16):2078-9 – reference: 25260700 - Bioinformatics. 2015 Jan 15;31(2):166-9 – reference: 24020486 - Genome Biol. 2013;14(9):R95 – reference: 24505276 - PLoS One. 2014;9(2):e87113 – reference: 22743226 - Bioinformatics. 2012 Aug 15;28(16):2184-5 – reference: 19675096 - Bioinformatics. 2009 Oct 1;25(19):2514-21 – reference: 21835007 - Genome Biol. 2011;12(8):R72 – reference: 19910308 - Bioinformatics. 2010 Jan 1;26(1):139-40 – reference: 11932250 - Genome Res. 2002 Apr;12(4):656-64 – reference: 19541911 - Genome Res. 2009 Sep;19(9):1639-45 – reference: 23497356 - BMC Bioinformatics. 2013;14:91 – reference: 24278466 - PLoS One. 2013;8(11):e81925 – reference: 21134889 - Bioinformatics. 2011 Jan 15;27(2):281-3 – reference: 20698981 - BMC Bioinformatics. 2010;11:422 – reference: 19880367 - Bioinformatics. 2010 Jan 1;26(1):127-9 – reference: 20979621 - Genome Biol. 2010;11(10):R106 – reference: 19289445 - Bioinformatics. 2009 May 1;25(9):1105-11 – reference: 23527012 - PLoS One. 2013;8(3):e58714 – reference: 23775753 - Methods Mol Biol. 2013;1034:385-403 – reference: 20110278 - Bioinformatics. 2010 Mar 15;26(6):841-2 – reference: 24300110 - Brief Bioinform. 2015 Jan;16(1):59-70 – reference: 19261174 - Genome Biol. 2009;10(3):R25 – reference: 20802226 - Nucleic Acids Res. 2010 Oct;38(18):e178 – reference: 16646809 - Stat Appl Genet Mol Biol. 2004;3:Article3 – reference: 24227677 - Bioinformatics. 2014 Apr 1;30(7):923-30
SSID	ssj0017805
Score	2.575848
Snippet	Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications,... Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a... Background Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications,... Doc number: 224 Abstract Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of... Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use...
SourceID	pubmedcentral proquest gale pubmed crossref springer
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	224
SubjectTerms	Algorithms Analysis Base Sequence Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Exons - genetics Gene Expression Profiling Genes Genomics - methods Health Facilities High-Throughput Nucleotide Sequencing - methods Humans Life Sciences Microarrays Research projects RNA sequencing Sequence Analysis, RNA - methods Software Transcriptome analysis
SummonAdditionalLinks	– databaseName: SpringerLink Contemporary (1997 - Present) dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3daxQxEA-1KvSlfrY9rbKKIAqhl83my7dDLAr2OO609C1kk6welL22dyf0v3dmv-geWtDnzLCzyUzmN2TyCyFvgmQuplCdQPERaJYXjuZDbLBBIjwTgvHV0wmnX9V4rM_OzGSLpO1dmKrbvT2SrHbqKqy1PGKwjVIoUARlgkLiuUPuQrLTGIzT2Wl3coAc_e1x5B-0eulncxO-kYU2OyQ3jkmr7HP84H_sfkh2G6yZjGrneES2YvmY3K9fn7x-QvjJaEKns3j5ITlx14uk5SdJJvMLvKUeEwC0yXQ8Spp-a_jqU_L9-NO3j59p84YC9QBlVtRxybQD1BYR_PEssqHPYINLQ0x5DE4UXDvPvFFBmWGAAjH3EUoOX5gw1Bnne2S7XJTxgCQyZzE1mdQ5LKAJ0uTCALrIsiBVkKIYkKN2aq1vCMbxnYtzWxUaWlqcCotTYZmwoDkg7zqNi5pc4xbZ17haFjkrSmyK-eHWy6X9MpvakeBgFjaHDcjbRqhYwKe9a-4YwA8gzVVP8rAnCUHl-8OtU9gmqJcWiYsAXQoDxrzqhlETG9XKuFjXMgDSGFe3yigtDVR2A7Jf-1n3-1ANK8BMoK16HtgJIB14f6Sc_6xowZHMTaDp71s_vGH6X2b12b8IPyc7ABczbJRL1SHZXl2t4wtyz_9azZdXL6sw_A1suifW priority: 102 providerName: Springer Nature
Title	MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing
URI	https://link.springer.com/article/10.1186/1471-2105-15-224 https://www.ncbi.nlm.nih.gov/pubmed/24972667 https://www.proquest.com/docview/1544435594 https://www.proquest.com/docview/1544741137 https://www.proquest.com/docview/1547869182 https://pubmed.ncbi.nlm.nih.gov/PMC4228501
Volume	15
WOSCitedRecordID	wos000338733500002&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: Open Access: BioMedCentral Open Access Titles 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: Advanced Technologies & Aerospace Database 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: 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 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 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 Central 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: Springer Journals New Starts & Take-Overs Collection 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/eLvHCXMwpV3di9QwEA_enYIvfutVz1JFEIWwm36l8UVWucNDdyldPVZfQpuk3oK0e9dd4f57Z_rldcF98aWwZMImnclkJvn1N4S80iFLjQvZCSQfmvpZntJsjAAbJMITWgtVl044-8Jns2ixEHF74Fa1sMrOJ9aOWpcKz8hHyBoDW3sg_PerC4pVo_B2tS2hsUcOmOsytPPPnPa3CMjX311NRuGIgSOmkOIElAXUdf3BVrTtkK_tSNtoya0r03onOrn7v3O4R-60MagzaYzmPrlhigfkVlOV8uoh8aaTmCZzc_HOmaZXpdPxljjxcoVfrxsHAl0nmU2cFocNo3xEvp0cf_34iba1FaiCEGdNUy9kUQrRnMGg0PMNGysfHJ-rjesZnQa5F6WKKcE1F2MNiWOmDKQiKhd6HPme95jsF2VhDokTZsy4wg-jDBQrdCiyQEDU4fs65DoMcouMutcsVUs8jvUvfsk6AYlCiYqRqBjJAgk9LfKm77FqSDd2yL5EzUnksigQLPMz3VSVPJ0nchJ4MCwEjVnkdSuUl_DXKm2_PYAJIP3VQPJoIAmLTQ2bO83KdrFX8q9aLfKib8aeCGArTLlpZCB4Yx7fKcOjUEDGZ5Enjc3104csmUMsBb35wBp7AaQJH7YUy_OaLhxJ3gIc-tvObq8N_R9v9enueT4jtyFw9BEy5_Ijsr--3Jjn5Kb6vV5WlzbZ4wtePyObHHw4nsWJXZ912PXytBFfG8MzDn5Ae3w6jb_Dr2R-9geO6Dyn
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLamAWIv3BmBAQGBEEhWG8eJYySEKmBa1YuqbqC9mcR2oBJKuqUF9U_xGzknl7JUom974NnHii-fzyU-_g4hL0zoxZZBdALBh6E8SWOadDHBBonwpDFSl6UTvgzFeBydnsrJDvndvIXBtMpGJ5aK2uQa_5F3kDUGTHsg-fv5GcWqUXi72pTQqGAxsKtfELIV7_ofYX9fMnb46eTDEa2rClANxn1BYz_0ohj8GIvukM-t19UcjjwzlvnWxEHqR7H2tBRGyK6BkCnRFpxwnUrTjTj-AAWVf4X7kUCu_oGg61sLrA_QXIVGYccDxU8hpAqoF1DGeMv0bRqACxZwMztz44q2tHyHN_-3NbtFbtQ-tturDsVtsmOzO-RaVXVzdZf4o96ETo_t2Vt3FK9yt-FlcSezOb7Oty448u503HPrPHNYlXvk86WM-D7ZzfLMPiBumHiWSR5GCQBXmlAmgQSvinMTChMGqUM6zbYqXROrY32PH6oMsKJQIRAUAkF5gYKeDnm97jGvSEW2yD5HpCjk6sgwGehbvCwK1T-eql7gw7AwKc4hr2qhNIdP67h-WwETQHqvluRBSxKUiW43N0hStTIr1F8YOeTZuhl7YoJeZvNlJQPOqeeLrTIiCiVEtA7ZrzC-nj7jUoCvCL1FC_1rAaRBb7dks-8lHTqS2AU49DfNObkw9H-s6sPt83xKrh-djIZq2B8PHpE9cJI5pgcycUB2F-dL-5hc1T8Xs-L8SakAXPL1sk_PH1XWjr8
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3rixMxEA96PvCL70f11FUEUQhtNptk47eiFg_vSmn1uG8hm2S1INt62wr33zuzL26LHoifM8NOkknym83kN4S88pLZEEN0AsGHp0mWW5qNMMEGifC099pVpROOD9V0mp6c6Fnzw61ss93bK8n6TQOyNBWb4drn9RJP5ZDBlkohWBGUCQqH0GVyJcGSQRitL467WwTk62-vJv-g1TuKdjfkcyfSbrbkzpVpdRJNbv1vH26Tmw0Gjca109whl0Jxl1yrq1Ke3SP8aDyj80X4-S46smerqOUtiWbLNb5eDxEA3Wg-HUdNHjZYcJ98nXz88v4TbWorUAcQZ0Mtlyy1gOYCgkKeBDZyCWx8sQ8xD96KnKfWMaeVV3rkIXDMXIBQxOXaj9KE8wdkr1gV4RGJZMZCrBOZZjCx2kudCQ2oI0m8VF6KfECG7TAb1xCPY_2LH6YKQFJpcCgMDoVhwoDmgLzpNNY16cYFsi9x5gxyWRSYLPPNbsvSHCzmZiw4mIVJYwPyuhHKV_BpZ5u3B9ABpL_qSe73JGGxuX5z6yCmWeylQUIjQJ1CgzEvumbUxAS2Iqy2tQyAN8bVhTIqlRoivgF5WPtc132IkhVgKdBWPW_sBJAmvN9SLL9XdOFI8ibQ9LetT54z_S-j-vhfhJ-T67MPE3N4MP38hNwARJlgLl2s9sne5nQbnpKr7tdmWZ4-q1bnb-G9M54
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=MAP-RSeq%3A+Mayo+Analysis+Pipeline+for+RNA+sequencing&rft.jtitle=BMC+bioinformatics&rft.au=Kalari%2C+Krishna+R&rft.au=Nair%2C+Asha+A&rft.au=Bhavsar%2C+Jaysheel+D&rft.au=O%27Brien%2C+Daniel+R&rft.date=2014-06-27&rft.pub=Springer+Nature+B.V&rft.eissn=1471-2105&rft.volume=15&rft_id=info:doi/10.1186%2F1471-2105-15-224&rft.externalDocID=3369317531
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