Two-pass alignment improves novel splice junction quantification. Veeneman, B. A., Shukla, S., Dhanasekaran, S. M., Chinnaiyan, A. M., & Nesvizhskii, A. I. Bioinformatics (Oxford, England), 32(1):43–49, January, 2016. doi abstract bibtex MOTIVATION: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. RESULTS: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. CONTACT: arul@med.umich.edu, nesvi@med.umich.edu AVAILABILITY AND IMPLEMENTATION: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
@article{veeneman_two-pass_2016,
title = {Two-pass alignment improves novel splice junction quantification.},
volume = {32},
copyright = {(c) The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.},
issn = {1367-4811 1367-4803},
doi = {10.1093/bioinformatics/btv642},
abstract = {MOTIVATION: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. RESULTS: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94\% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. CONTACT: arul@med.umich.edu, nesvi@med.umich.edu AVAILABILITY AND IMPLEMENTATION: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
language = {eng},
number = {1},
journal = {Bioinformatics (Oxford, England)},
author = {Veeneman, Brendan A. and Shukla, Sudhanshu and Dhanasekaran, Saravana M. and Chinnaiyan, Arul M. and Nesvizhskii, Alexey I.},
month = jan,
year = {2016},
pmid = {26519505},
pmcid = {PMC5006238},
keywords = {Base Sequence, Cell Line, Tumor, Databases, Nucleic Acid, Humans, RNA Splice Sites/*genetics, RNA Splicing/*genetics, Sequence Alignment/*methods},
pages = {43--49}
}
Downloads: 0
{"_id":"sWBSvvt5BsFZgj2FX","bibbaseid":"veeneman-shukla-dhanasekaran-chinnaiyan-nesvizhskii-twopassalignmentimprovesnovelsplicejunctionquantification-2016","downloads":0,"creationDate":"2019-01-31T19:37:48.304Z","title":"Two-pass alignment improves novel splice junction quantification.","author_short":["Veeneman, B. A.","Shukla, S.","Dhanasekaran, S. M.","Chinnaiyan, A. M.","Nesvizhskii, A. I."],"year":2016,"bibtype":"article","biburl":"https://api.zotero.org/groups/2283367/items?key=x55htG8stHNuPk22YQR31JQa&format=bibtex&limit=100","bibdata":{"bibtype":"article","type":"article","title":"Two-pass alignment improves novel splice junction quantification.","volume":"32","copyright":"(c) The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.","issn":"1367-4811 1367-4803","doi":"10.1093/bioinformatics/btv642","abstract":"MOTIVATION: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. RESULTS: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. CONTACT: arul@med.umich.edu, nesvi@med.umich.edu AVAILABILITY AND IMPLEMENTATION: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","language":"eng","number":"1","journal":"Bioinformatics (Oxford, England)","author":[{"propositions":[],"lastnames":["Veeneman"],"firstnames":["Brendan","A."],"suffixes":[]},{"propositions":[],"lastnames":["Shukla"],"firstnames":["Sudhanshu"],"suffixes":[]},{"propositions":[],"lastnames":["Dhanasekaran"],"firstnames":["Saravana","M."],"suffixes":[]},{"propositions":[],"lastnames":["Chinnaiyan"],"firstnames":["Arul","M."],"suffixes":[]},{"propositions":[],"lastnames":["Nesvizhskii"],"firstnames":["Alexey","I."],"suffixes":[]}],"month":"January","year":"2016","pmid":"26519505","pmcid":"PMC5006238","keywords":"Base Sequence, Cell Line, Tumor, Databases, Nucleic Acid, Humans, RNA Splice Sites/*genetics, RNA Splicing/*genetics, Sequence Alignment/*methods","pages":"43–49","bibtex":"@article{veeneman_two-pass_2016,\n\ttitle = {Two-pass alignment improves novel splice junction quantification.},\n\tvolume = {32},\n\tcopyright = {(c) The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.},\n\tissn = {1367-4811 1367-4803},\n\tdoi = {10.1093/bioinformatics/btv642},\n\tabstract = {MOTIVATION: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. RESULTS: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94\\% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. CONTACT: arul@med.umich.edu, nesvi@med.umich.edu AVAILABILITY AND IMPLEMENTATION: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Bioinformatics (Oxford, England)},\n\tauthor = {Veeneman, Brendan A. and Shukla, Sudhanshu and Dhanasekaran, Saravana M. and Chinnaiyan, Arul M. and Nesvizhskii, Alexey I.},\n\tmonth = jan,\n\tyear = {2016},\n\tpmid = {26519505},\n\tpmcid = {PMC5006238},\n\tkeywords = {Base Sequence, Cell Line, Tumor, Databases, Nucleic Acid, Humans, RNA Splice Sites/*genetics, RNA Splicing/*genetics, Sequence Alignment/*methods},\n\tpages = {43--49}\n}\n\n","author_short":["Veeneman, B. A.","Shukla, S.","Dhanasekaran, S. M.","Chinnaiyan, A. M.","Nesvizhskii, A. I."],"key":"veeneman_two-pass_2016","id":"veeneman_two-pass_2016","bibbaseid":"veeneman-shukla-dhanasekaran-chinnaiyan-nesvizhskii-twopassalignmentimprovesnovelsplicejunctionquantification-2016","role":"author","urls":{},"keyword":["Base Sequence","Cell Line","Tumor","Databases","Nucleic Acid","Humans","RNA Splice Sites/*genetics","RNA Splicing/*genetics","Sequence Alignment/*methods"],"downloads":0},"search_terms":["two","pass","alignment","improves","novel","splice","junction","quantification","veeneman","shukla","dhanasekaran","chinnaiyan","nesvizhskii"],"keywords":["base sequence","cell line","tumor","databases","nucleic acid","humans","rna splice sites/*genetics","rna splicing/*genetics","sequence alignment/*methods"],"authorIDs":["54de1f9050a3f8a90a00064c"],"dataSources":["iyKKecEnSYbLzkPEN"]}