Loading…
ASEQ: fast allele-specific studies from next-generation sequencing data
Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide...
Saved in:
| Published in: | BMC medical genomics 2015-03, Vol.8 (1), p.9-9, Article 9 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263 |
|---|---|
| cites | cdi_FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263 |
| container_end_page | 9 |
| container_issue | 1 |
| container_start_page | 9 |
| container_title | BMC medical genomics |
| container_volume | 8 |
| creator | Romanel, Alessandro Lago, Sara Prandi, Davide Sboner, Andrea Demichelis, Francesca |
| description | Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives.
We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease.
ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines. |
| doi_str_mv | 10.1186/s12920-015-0084-2 |
| format | article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4363342</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A541467421</galeid><sourcerecordid>A541467421</sourcerecordid><originalsourceid>FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263</originalsourceid><addsrcrecordid>eNp1kl9r1jAUxoM43Hz1A3gjBW-2i878b-qF8LLNbTAQnV6HND2pkTZ5bdqh3968dI4VJrlIOOd3Hs55chB6Q_ApIUq-T4TWFJeYiBJjxUv6DB2RSohSVTV__uh9iF6m9BNjiUVNXqBDKpSqGauP0OX29uLLh8KZNBWm76GHMu3Aeudtkaa59ZAKN8ahCPB7KjsIMJrJx1Ak-DVDsD50RWsm8wodONMneH1_b9D3Txffzq7Km8-X12fbm7IRkkylc7WjtmoE4Vg0FWuFqLiiilKGpa3aSpmWWawY5zkhoW4AuGyVrSrpFJVsgz4uuru5GaC1EKbR9Ho3-sGMf3Q0Xq8zwf_QXbzTnEnGOM0C54tA4-N_BNYZGwe9GK2z0XpvtN7LHN_3McZsRJr04JOFvjcB4pw0kbl9JXh2eYPeLWhnetA-uJh17R7XW8EJzyQlmTp9gsqnhcHbGMD5HF8VnKwKMjPlP-rMnJK-vv26ZsnC2jGmNIJ7mJdgvV-lJyd8-9jph4p_u8P-AleXwoI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1674685493</pqid></control><display><type>article</type><title>ASEQ: fast allele-specific studies from next-generation sequencing data</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Romanel, Alessandro ; Lago, Sara ; Prandi, Davide ; Sboner, Andrea ; Demichelis, Francesca</creator><creatorcontrib>Romanel, Alessandro ; Lago, Sara ; Prandi, Davide ; Sboner, Andrea ; Demichelis, Francesca</creatorcontrib><description>Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives.
We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease.
ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines.</description><identifier>ISSN: 1755-8794</identifier><identifier>EISSN: 1755-8794</identifier><identifier>DOI: 10.1186/s12920-015-0084-2</identifier><identifier>PMID: 25889339</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Alleles ; Analysis ; Cancer ; Computational Biology - methods ; Computer Systems ; Databases, Factual ; Gene Expression Profiling ; Genes ; Genes, Neoplasm ; Genetic aspects ; Genome, Human ; Genomics ; Genotype ; Heterozygote ; High-Throughput Nucleotide Sequencing - methods ; Humans ; Male ; Medical research ; Medicine, Experimental ; Polymorphism, Single Nucleotide ; Programming Languages ; Prostate cancer ; Prostatic Neoplasms - genetics ; Prostatic Neoplasms - metabolism ; Sequence Analysis, DNA - methods ; Software ; Technology application ; User-Computer Interface</subject><ispartof>BMC medical genomics, 2015-03, Vol.8 (1), p.9-9, Article 9</ispartof><rights>COPYRIGHT 2015 BioMed Central Ltd.</rights><rights>Romanel et al.; licensee BioMed Central. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263</citedby><cites>FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4363342/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4363342/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,733,786,790,891,27957,27958,37048,53827,53829</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25889339$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Romanel, Alessandro</creatorcontrib><creatorcontrib>Lago, Sara</creatorcontrib><creatorcontrib>Prandi, Davide</creatorcontrib><creatorcontrib>Sboner, Andrea</creatorcontrib><creatorcontrib>Demichelis, Francesca</creatorcontrib><title>ASEQ: fast allele-specific studies from next-generation sequencing data</title><title>BMC medical genomics</title><addtitle>BMC Med Genomics</addtitle><description>Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives.
We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease.
ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines.</description><subject>Alleles</subject><subject>Analysis</subject><subject>Cancer</subject><subject>Computational Biology - methods</subject><subject>Computer Systems</subject><subject>Databases, Factual</subject><subject>Gene Expression Profiling</subject><subject>Genes</subject><subject>Genes, Neoplasm</subject><subject>Genetic aspects</subject><subject>Genome, Human</subject><subject>Genomics</subject><subject>Genotype</subject><subject>Heterozygote</subject><subject>High-Throughput Nucleotide Sequencing - methods</subject><subject>Humans</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Programming Languages</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - genetics</subject><subject>Prostatic Neoplasms - metabolism</subject><subject>Sequence Analysis, DNA - methods</subject><subject>Software</subject><subject>Technology application</subject><subject>User-Computer Interface</subject><issn>1755-8794</issn><issn>1755-8794</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp1kl9r1jAUxoM43Hz1A3gjBW-2i878b-qF8LLNbTAQnV6HND2pkTZ5bdqh3968dI4VJrlIOOd3Hs55chB6Q_ApIUq-T4TWFJeYiBJjxUv6DB2RSohSVTV__uh9iF6m9BNjiUVNXqBDKpSqGauP0OX29uLLh8KZNBWm76GHMu3Aeudtkaa59ZAKN8ahCPB7KjsIMJrJx1Ak-DVDsD50RWsm8wodONMneH1_b9D3Txffzq7Km8-X12fbm7IRkkylc7WjtmoE4Vg0FWuFqLiiilKGpa3aSpmWWawY5zkhoW4AuGyVrSrpFJVsgz4uuru5GaC1EKbR9Ho3-sGMf3Q0Xq8zwf_QXbzTnEnGOM0C54tA4-N_BNYZGwe9GK2z0XpvtN7LHN_3McZsRJr04JOFvjcB4pw0kbl9JXh2eYPeLWhnetA-uJh17R7XW8EJzyQlmTp9gsqnhcHbGMD5HF8VnKwKMjPlP-rMnJK-vv26ZsnC2jGmNIJ7mJdgvV-lJyd8-9jph4p_u8P-AleXwoI</recordid><startdate>20150301</startdate><enddate>20150301</enddate><creator>Romanel, Alessandro</creator><creator>Lago, Sara</creator><creator>Prandi, Davide</creator><creator>Sboner, Andrea</creator><creator>Demichelis, Francesca</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20150301</creationdate><title>ASEQ: fast allele-specific studies from next-generation sequencing data</title><author>Romanel, Alessandro ; Lago, Sara ; Prandi, Davide ; Sboner, Andrea ; Demichelis, Francesca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Alleles</topic><topic>Analysis</topic><topic>Cancer</topic><topic>Computational Biology - methods</topic><topic>Computer Systems</topic><topic>Databases, Factual</topic><topic>Gene Expression Profiling</topic><topic>Genes</topic><topic>Genes, Neoplasm</topic><topic>Genetic aspects</topic><topic>Genome, Human</topic><topic>Genomics</topic><topic>Genotype</topic><topic>Heterozygote</topic><topic>High-Throughput Nucleotide Sequencing - methods</topic><topic>Humans</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Programming Languages</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - genetics</topic><topic>Prostatic Neoplasms - metabolism</topic><topic>Sequence Analysis, DNA - methods</topic><topic>Software</topic><topic>Technology application</topic><topic>User-Computer Interface</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Romanel, Alessandro</creatorcontrib><creatorcontrib>Lago, Sara</creatorcontrib><creatorcontrib>Prandi, Davide</creatorcontrib><creatorcontrib>Sboner, Andrea</creatorcontrib><creatorcontrib>Demichelis, Francesca</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC medical genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Romanel, Alessandro</au><au>Lago, Sara</au><au>Prandi, Davide</au><au>Sboner, Andrea</au><au>Demichelis, Francesca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ASEQ: fast allele-specific studies from next-generation sequencing data</atitle><jtitle>BMC medical genomics</jtitle><addtitle>BMC Med Genomics</addtitle><date>2015-03-01</date><risdate>2015</risdate><volume>8</volume><issue>1</issue><spage>9</spage><epage>9</epage><pages>9-9</pages><artnum>9</artnum><issn>1755-8794</issn><eissn>1755-8794</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives.
We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease.
ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>25889339</pmid><doi>10.1186/s12920-015-0084-2</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1755-8794 |
| ispartof | BMC medical genomics, 2015-03, Vol.8 (1), p.9-9, Article 9 |
| issn | 1755-8794 1755-8794 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4363342 |
| source | Publicly Available Content Database; PubMed Central |
| subjects | Alleles Analysis Cancer Computational Biology - methods Computer Systems Databases, Factual Gene Expression Profiling Genes Genes, Neoplasm Genetic aspects Genome, Human Genomics Genotype Heterozygote High-Throughput Nucleotide Sequencing - methods Humans Male Medical research Medicine, Experimental Polymorphism, Single Nucleotide Programming Languages Prostate cancer Prostatic Neoplasms - genetics Prostatic Neoplasms - metabolism Sequence Analysis, DNA - methods Software Technology application User-Computer Interface |
| title | ASEQ: fast allele-specific studies from next-generation sequencing data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T07%3A35%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ASEQ:%20fast%20allele-specific%20studies%20from%20next-generation%20sequencing%20data&rft.jtitle=BMC%20medical%20genomics&rft.au=Romanel,%20Alessandro&rft.date=2015-03-01&rft.volume=8&rft.issue=1&rft.spage=9&rft.epage=9&rft.pages=9-9&rft.artnum=9&rft.issn=1755-8794&rft.eissn=1755-8794&rft_id=info:doi/10.1186/s12920-015-0084-2&rft_dat=%3Cgale_pubme%3EA541467421%3C/gale_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-b561t-ff9f2c7b51405b73d557482822306c7d78ad3c083445746e9bee46d8c776f8263%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1674685493&rft_id=info:pmid/25889339&rft_galeid=A541467421&rfr_iscdi=true |