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SAUTE: sequence assembly using target enrichment

BACKGROUND: Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multip...

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Autores principales: Souvorov, Alexandre, Agarwala, Richa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293564/
https://www.ncbi.nlm.nih.gov/pubmed/34289805
http://dx.doi.org/10.1186/s12859-021-04174-9
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author Souvorov, Alexandre
Agarwala, Richa
author_facet Souvorov, Alexandre
Agarwala, Richa
author_sort Souvorov, Alexandre
collection PubMed
description BACKGROUND: Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multiple well supported variants for assembled regions. RESULTS: To facilitate assembly of repeat regions and to report multiple well supported variants when a user can provide target sequences to assist the assembly, we propose SAUTE and SAUTE_PROT assemblers. Both assemblers use de Bruijn graph on reads. Targets can be transcripts or proteins for RNA-seq reads and transcripts, proteins, or genomic regions for genomic reads. Target sequences are nucleotide and protein sequences for SAUTE and SAUTE_PROT, respectively. CONCLUSIONS: For RNA-seq, comparisons with Trinity, rnaSPAdes, SPAligner, and SPAdes assembly of reads aligned to target proteins by DIAMOND show that SAUTE_PROT finds more coding sequences that translate to benchmark proteins. Using AMRFinderPlus calls, we find SAUTE has higher sensitivity and precision than SPAdes, plasmidSPAdes, SPAligner, and SPAdes assembly of reads aligned to target regions by HISAT2. It also has better sensitivity than SKESA but worse precision. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04174-9.
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spelling pubmed-82935642021-07-21 SAUTE: sequence assembly using target enrichment Souvorov, Alexandre Agarwala, Richa BMC Bioinformatics Software BACKGROUND: Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multiple well supported variants for assembled regions. RESULTS: To facilitate assembly of repeat regions and to report multiple well supported variants when a user can provide target sequences to assist the assembly, we propose SAUTE and SAUTE_PROT assemblers. Both assemblers use de Bruijn graph on reads. Targets can be transcripts or proteins for RNA-seq reads and transcripts, proteins, or genomic regions for genomic reads. Target sequences are nucleotide and protein sequences for SAUTE and SAUTE_PROT, respectively. CONCLUSIONS: For RNA-seq, comparisons with Trinity, rnaSPAdes, SPAligner, and SPAdes assembly of reads aligned to target proteins by DIAMOND show that SAUTE_PROT finds more coding sequences that translate to benchmark proteins. Using AMRFinderPlus calls, we find SAUTE has higher sensitivity and precision than SPAdes, plasmidSPAdes, SPAligner, and SPAdes assembly of reads aligned to target regions by HISAT2. It also has better sensitivity than SKESA but worse precision. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04174-9. BioMed Central 2021-07-21 /pmc/articles/PMC8293564/ /pubmed/34289805 http://dx.doi.org/10.1186/s12859-021-04174-9 Text en © This is a U.S. government work and not under copyright protection in the U.S; foreign copyright protection may apply 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Souvorov, Alexandre
Agarwala, Richa
SAUTE: sequence assembly using target enrichment
title SAUTE: sequence assembly using target enrichment
title_full SAUTE: sequence assembly using target enrichment
title_fullStr SAUTE: sequence assembly using target enrichment
title_full_unstemmed SAUTE: sequence assembly using target enrichment
title_short SAUTE: sequence assembly using target enrichment
title_sort saute: sequence assembly using target enrichment
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293564/
https://www.ncbi.nlm.nih.gov/pubmed/34289805
http://dx.doi.org/10.1186/s12859-021-04174-9
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