Boiler: lossy compression of RNA-seq alignments using coverage vectors

Pritt, Jacob; Langmead, Ben

doi:10.1093/nar/gkw540

Cited by 5 publications

(2 citation statements)

References 24 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This conversion (which could be applied as a preprocessing step to all reads) works well especially if the insert size (the distance between between the two ends) has limited variation across the reads. Additional ideas for handling paired-end reads are discussed in, for instance 29 .…”

Section: Methodsmentioning

confidence: 99%

Optimal compressed representation of high throughput sequence data via light assembly

et al. 2018

View full text Add to dashboard Cite

The most effective genomic data compression methods either assemble reads into contigs, or replace them with their alignment positions on a reference genome. Such methods require significant computational resources, but faster alternatives that avoid using explicit or de novo-constructed references fail to match their performance. Here, we introduce a new reference-free compressed representation for genomic data based on light de novo assembly of reads, where each read is represented as a node in a (compact) trie. We show how to efficiently build such tries to compactly represent reads and demonstrate that among all methods using this representation (including all de novo assembly based methods), our method achieves the shortest possible output. We also provide an lower bound on the compression rate achievable on uniformly sampled genomic read data, which is approximated by our method well. Our method significantly improves the compression performance of alternatives without compromising speed.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimal compressed representation of high throughput sequence data via light assembly

et al. 2018

View full text Add to dashboard Cite

show abstract

“…These BAM files are extremely large and require sophisticated storage solutions. This is because the format stores data on a per-read basis and the space requirement grows almost linearly with the number of reads [55]. A BAM file for a 30x whole genome requires about 80-90 gigabytes of storage.…”

Section: Introductionmentioning

confidence: 99%

Findel: A Deep Learning Approach To Efficient Artifact Removal from Cancer Genomes

2022

SCRI

View full text Add to dashboard Cite

Next-generation sequencing technologies have increased sequencing throughput by 100-1000 folds and subsequently reduced the cost of sequencing a human genome to approximately US$1,000. However, the existence of sequencing artifacts can cause erroneous identification of variants and adversely impact the downstream analyses. Currently, the manual inspection of variants for additional refinement is still necessary for high-quality variant calls. The inspection is usually done on large binary alignment map (BAM) files which consume a huge amount of labor and time. It also suffers from a lack of standardization and reproducibility. Here we show that the use of mutational signatures coupled with deep learning can replace the current standards in the bioinformatics workflow. This software, called FINDEL, can efficiently remove sequencing artifacts from cancer samples. It queries the variant call format file which is much more compact than BAM files. The software automates the variant refinement process and produces high-quality variant calls.

show abstract