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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Bibliographic Details
Main Authors: Agarwala, R. (Author), Souvorov, A. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
RNA
Online Access:View Fulltext in Publisher
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020 |a 14712105 (ISSN) 
245 1 0 |a SAUTE: sequence assembly using target enrichment 
260 0 |b BioMed Central Ltd  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1186/s12859-021-04174-9 
520 3 |a 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. © 2021, This is a U.S. government work and not under copyright protection in the U.S; foreign copyright protection may apply. 
650 0 4 |a algorithm 
650 0 4 |a Algorithms 
650 0 4 |a Antimicrobial resistance 
650 0 4 |a Carry-over contamination 
650 0 4 |a Coding sequences 
650 0 4 |a de Bruijn graphs 
650 0 4 |a De Bruijn graphs 
650 0 4 |a De-novo assembly 
650 0 4 |a DNA sequence 
650 0 4 |a genome 
650 0 4 |a Genome 
650 0 4 |a Genomic regions 
650 0 4 |a genomics 
650 0 4 |a Genomics 
650 0 4 |a high throughput sequencing 
650 0 4 |a High-Throughput Nucleotide Sequencing 
650 0 4 |a Illumina reads 
650 0 4 |a Protein sequences 
650 0 4 |a Proteins 
650 0 4 |a RNA 
650 0 4 |a RNA-seq 
650 0 4 |a RNA-Seq 
650 0 4 |a Sequence Analysis, DNA 
650 0 4 |a Sequence assemblies 
650 0 4 |a Shovels 
650 0 4 |a Target proteins 
650 0 4 |a Target sequences 
700 1 |a Agarwala, R.  |e author 
700 1 |a Souvorov, A.  |e author 
773 |t BMC Bioinformatics