Biology subjects

Schatz, M.

Publications and source records attributed to Schatz, M..

28 records · Page 2Linked to original sources

bioRxiv2021

Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

Nanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject "non-target" DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing with the help of efficient pangenome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics (half-maximal exact matches) in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONIs index and peak memory footprint are also 15 to 4 times smaller than minimap2, respectively. These improvements become even more pronounced with even larger reference databases; SPUMONIs index size scales sublinearly with the number of reference genomes included. This could enable accurate targeted sequencing even in the case where the targeted strains have not necessarily been sequenced or assembled previously. SPUMONI is open source software available from https://github.com/oma219/spumoni.

bioinformatics↗

bioRxiv2021

The dynamic trophic architecture of open-ocean protist communities revealed through machine- guided metatranscriptomics

Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet, the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here we introduce a machine learning model that can predict the in situ nutritional mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic status. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. In addition, metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodelled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling the technique introduced here with experiments designed to reveal the mechanisms driving mixotroph physiology is a promising approach for understanding the ecology of mixotrophic populations in the natural environment. Significance statementMixotrophy is a ubiquitous nutritional strategy in marine ecosystems. Although our understanding of the distribution and abundance of mixotrophic plankton has improved significantly, the functional roles of mixotrophs are difficult to pinpoint, as mixotroph nutritional strategies are flexible and form a continuum between heterotrophy and phototrophy. We employ a machine learning-driven metatranscriptomic technique to assess the nutritional strategies of abundant planktonic populations in situ and demonstrate that mixotrophic populations play varying functional roles along physico-chemical gradients in the North Pacific Ocean, revealing a degree of physiological plasticity unique to aquatic mixotrophs. Our results highlight mechanisms that may dictate the flow of biogeochemical elements and the ecology of the North Pacific Ocean, one of the largest biogeographical provinces on Earth.

ecology↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗

bioRxiv2020

Sapling: Accelerating Suffix Array Queries with Learned Data Models

MotivationAs genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query it requires widespread memory accesses, causing a large number of cache misses on large datasets. ResultsHere we present Sapling, an algorithm for sequence alignment which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple existing read aligners on a wide collection of genomes, including human, bacteria, and plants, speeding up the algorithm by more than a factor of two while adding less than 1% to the suffix arrays memory footprint. Availability and implementationThe source code and tutorial are available open-source at https://github.com/mkirsche/sapling. Supplementary InformationSupplementary notes and figures are available online.

bioinformatics↗

bioRxiv2019

First near complete haplotype phased genome assembly of River buffalo (Bubalus bubalis)

This study reports the first haplotype phased reference quality genome assembly of Murrah an Indian breed of river buffalo. A mother-father-progeny trio was used for sequencing so that the individual haplotypes could be assembled in the progeny. Parental DNA samples were sequenced on the Illumina platform to generate a total of 274 Gb paired-end data. The progeny DNA sample was sequenced using PacBio long reads and 10x Genomics linked reads at 166x coverage along with 802Gb of optical mapping data. Trio binning based FALCON assembly of each haplotype was scaffolded with 10x Genomics reads and super-scaffolded with BioNano Maps to build reference quality assembly of sire and dam haplotypes of 2.63Gb and 2.64Gb with just 59 and 64 scaffolds and N50 of 81.98Mb and 83.23Mb, respectively. BUSCO single copy core gene set coverage was > 91.25%, and gVolante-CEGMA completeness was >96.14% for both haplotypes. Finally, RaGOO was used to order and build the chromosomal level assembly with 25 scaffolds and N50 of 117.48 Mb (sire haplotype) and 118.51 Mb (dam haplotype). The improved haplotype phased genome assembly of river buffalo may provide valuable resources to discover molecular mechanisms related to milk production and reproduction traits.

genomics↗

bioRxiv2018

Skyhawk: An Artificial Neural Network-based discriminator for reviewing clinically significant genomic variants

MotivationMany rare diseases and cancers are fundamentally diseases of the genome. In the past several years, genome sequencing has become one of the most important tools in clinical practice for rare disease diagnosis and targeted cancer therapy. However, variant interpretation remains the bottleneck as is not yet automated and may take a specialist several hours of work per patient. On average, one-fifth of this time is spent on visually confirming the authenticity of the candidate variants. ResultsWe developed Skyhawk, an artificial neural network-based discriminator that mimics the process of expert review on clinically significant genomics variants. Skyhawk runs in less than one minute to review ten thousand variants, and about 30 minutes to review all variants in a typical whole-genome sequencing sample. Among the false positive singletons identified by GATK HaplotypeCaller, UnifiedGenotyper and 16GT in the HG005 GIAB sample, 79.7% were rejected by Skyhawk. Worked on the Variants with Unknown Significance (VUS), Skyhawk marked most of the false positive variants for manual review and most of the true positive variants no need for review. AvailabilitySkyhawk is easy to use and freely available at https://github.com/aquaskyline/Skyhawk

bioinformatics↗

bioRxiv2018

Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5%-15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieved 99.73%, 97.68% and 95.36% precision on known variants, and 98.65%, 92.57%, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than two hours on a standard server. Furthermore, we identified 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model.

bioinformatics↗

bioRxiv2017

REASSESSING THE REVOLUTIONS RESOLUTIONS

We are currently facing an avalanche of cryo-EM (cryogenic Electron Microscopy) publications presenting beautiful structures at resolution levels of ~3[A]: a true \"resolution revolution\" [Kuhlbrandt, Science 343(2014)1443-1444]. Impressive as these results may be, a fundamental statistical error has persisted in the literature that affects the numerical resolution values for practically all published structures. The error goes back to a misinterpretation of basic statistics and pervades virtually all popular cryo-EM quality metrics. The resolution in cryo-EM is typically assessed by the Fourier Shell Correlation \"FSC\" [Harauz & van Heel: Optik 73(1986)146-156] using a fixed threshold value of 0.143 (\"FSC0.143\") [Rosenthal, Henderson, J. Mol. Biol. 333(2003)721-745]. Using a simple model experiment we illustrate why this fixed threshold is flawed and we pinpoint the source of the resolution confusion. When two vectors are uncorrelated the expectation value of their inner-product is zero. That, however, does not imply that each individual inner-product of the vectors is zero (the vectors are not orthogonal). This error was introduced to electron microscopy in [Frank & Al-Ali, Nature 256(1975)376-379] and has since proliferated into virtually all quality and resolution-related metrics in EM. One criterion not affected by this error is the information-based [1/2]-bit FSC threshold [van Heel & Schatz: J. Struct. Biol. 151(2005)250-262].

biochemistry↗

bioRxiv2017

Accurate detection of complex structural variations using single molecule sequencing

Structural variations (SVs) are the largest source of genetic variation, but remain poorly understood because of limited genomics technology. Single molecule long read sequencing from Pacific Biosciences and Oxford Nanopore has the potential to dramatically advance the field, although their high error rates challenge existing methods. Addressing this need, we introduce open-source methods for long read alignment (NGMLR, https://github.com/philres/ngmlr) and SV identification (Sniffles, https://github.com/fritzsedlazeck/Sniffles) that enable unprecedented SV sensitivity and precision, including within repeat-rich regions and of complex nested events that can have significant impact on human disorders. Examining several datasets, including healthy and cancerous human genomes, we discover thousands of novel variants using long reads and categorize systematic errors in short-read approaches. NGMLR and Sniffles are further able to automatically filter false events and operate on low amounts of coverage to address the cost factor that has hindered the application of long reads in clinical and research settings.

bioinformatics↗

bioRxiv2017

Proper experimental design requires randomization/balancing of molecular ecology experiments

Properly designed (randomized and/or balanced) experiments are standard in ecological research. Molecular methods are increasingly used in ecology, but studies generally do not report the detailed design of sample processing in the laboratory. This may strongly influence the interpretability of results if the laboratory procedures do not account for the confounding effects of unexpected laboratory events. We demonstrate this with a simple experiment where unexpected differences in laboratory processing of samples would have biased results if randomization in DNA extraction and PCR steps do not provide safeguards. We emphasize the need for proper experimental design and reporting of the laboratory phase of molecular ecology research to ensure the reliability and interpretability of results.

ecology↗