CONSULT: accurate contamination removal using locality-sensitive hashing

Abstract A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skim-based mitochondrial assemblies.

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CONSULT: Accurate contamination removal using locality-sensitive hashing

10.1101/2021.03.18.436035 ◽

2021 ◽

Author(s):

Eleonora Rachtman ◽

Vineet Bafna ◽

Siavash Mirarab

Keyword(s):

Genome Sequencing ◽

Fundamental Question ◽

Locality Sensitive Hashing ◽

True Positive ◽

Reference Dataset ◽

Large Dataset ◽

Organelle Genome ◽

Distance Calculation ◽

Large Memory ◽

Low Coverage

A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skims-based mitochondrial assemblies.

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353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

Journal of Animal Science ◽

10.1093/jas/skaa278.149 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 81-82

Author(s):

Joaquim Casellas ◽

Melani Martín de Hijas-Villalba ◽

Marta Vázquez-Gómez ◽

Samir Id Lahoucine

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Error Rate ◽

Allele Frequencies ◽

Paternity Testing ◽

Sequencing Error ◽

Whole Genome ◽

Genomic Evaluation ◽

Sequencing Error Rate ◽

Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

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Batch effects in population genomic studies with low‐coverage whole genome sequencing data: causes, detection, and mitigation

Molecular Ecology Resources ◽

10.1111/1755-0998.13559 ◽

2021 ◽

Author(s):

Runyang Nicolas Lou ◽

Nina Overgaard Therkildsen

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Batch Effects ◽

Sequencing Data ◽

Population Genomic ◽

Genomic Studies ◽

Low Coverage

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Identifying tumor clones in sparse single-cell mutation data

Bioinformatics ◽

10.1093/bioinformatics/btaa449 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i186-i193

Author(s):

Matthew A Myers ◽

Simone Zaccaria ◽

Benjamin J Raphael

Keyword(s):

Single Cell ◽

Genome Sequencing ◽

Whole Genome ◽

Sequencing Data ◽

Single Nucleotide ◽

Sequencing Coverage ◽

Sequencing Technologies ◽

Low Coverage ◽

Clonal Composition ◽

Cancer Studies

Abstract Motivation Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. Results We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. Availability and implementation SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. Supplementary information Supplementary data are available at Bioinformatics online.

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