Details

Autor(en) / Beteiligte

• Baid, Gunjan
• Cook, Daniel E
• Shafin, Kishwar
• Yun, Taedong
• Llinares-López, Felipe
• Berthet, Quentin
• Belyaeva, Anastasiya
• Töpfer, Armin
• Wenger, Aaron M
• Rowell, William J
• Yang, Howard
• Kolesnikov, Alexey
• Ammar, Waleed
• Vert, Jean-Philippe
• Vaswani, Ashish
• McLean, Cory Y
• Nattestad, Maria
• Chang, Pi-Chuan
• Carroll, Andrew

Titel

DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer

Ist Teil von

• Nature biotechnology, 2023-02, Vol.41 (2), p.232-238

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Circular consensus sequencing with Pacific Biosciences (PacBio) technology generates long (10-25 kilobases), accurate 'HiFi' reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation, pbccs, uses a hidden Markov model. We introduce DeepConsensus, which uses an alignment-based loss to train a gap-aware transformer-encoder for sequence correction. Compared to pbccs, DeepConsensus reduces read errors by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27% and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9 megabases (Mb) to 17.2 Mb), increase gene completeness (94% to 97%), reduce the false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45) and reduce variant-calling errors by 24%. DeepConsensus models could be trained to the general problem of analyzing the alignment of other types of sequences, such as unique molecular identifiers or genome assemblies.