Using large sequencing data sets to refine intragenic disease regions and prioritize clinical variant interpretation

Genetic variation in populations of Middle Eastern origin remains highly underrepresented in most comprehensive genomic databases. This underrepresentation hampers the functional annotation of the human genome, and also challenges accurate clinical variant interpretation. To highlight the importance of capturing genetic variation in the Middle East, we aggregate whole exome and genome sequencing data from 2,116 individuals in the Middle East and establish the Middle East Variation (MEV) database. Of the high impact coding variants in this database, 34% were absent from the most comprehensive Genome Aggregation Database (gnomAD), thus representing unique Middle Eastern variation which might directly impact clinical variant interpretation. We highlight 167 variants with MAF >1% in the MEV database which were previously reported as rare disease variants in ClinVar and the Human Gene Mutation Database (HGMD). Furthermore, the MEV database consisted of 365 homozygous loss of function (LoF) variants, the majority of which (239/365, 65.5%) were absent from gnomAD, representing complete knockouts of 229 unique genes in reportedly healthy individuals. Intriguingly, 58 of those genes have several clinically significant variants reported in ClinVar and HGMD. Our study shows that genetic variation in the Middle East improves functional annotation and clinical interpretation of the genome and emphasizes the need for expanding sequencing studies in the Middle East and other underrepresented populations.

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Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Microbiology Research ◽

10.3390/microbiolres12020022 ◽

2021 ◽

Vol 12 (2) ◽

pp. 317-334

Author(s):

Omar Alaqeeli ◽

Li Xing ◽

Xuekui Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Classification Tree ◽

Area Under The Curve ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Tree Algorithms ◽

R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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Refinement of the clinical variant interpretation framework by statistical evidence and machine learning

Med ◽

10.1016/j.medj.2021.02.003 ◽

2021 ◽

Author(s):

Atsushi Takata ◽

Kohei Hamanaka ◽

Naomichi Matsumoto

Keyword(s):

Machine Learning ◽

Statistical Evidence ◽

Variant Interpretation ◽

Clinical Variant

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Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽

10.3897/mycokeys.39.28109 ◽

2018 ◽

Vol 39 ◽

pp. 29-40 ◽

Cited By ~ 21

Author(s):

Sten Anslan ◽

R. Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Operational Taxonomic Units ◽

High Throughput Sequencing Data ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

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Computational Tools for Identification of microRNAs in Deep Sequencing Data Sets

Computational Biology and Applied Bioinformatics ◽

10.5772/21173 ◽

2011 ◽

Cited By ~ 1

Author(s):

Manuel A. S. Santos ◽

Ana Raquel

Keyword(s):

Deep Sequencing ◽

Data Sets ◽

Sequencing Data ◽

Computational Tools ◽

Deep Sequencing Data

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ExAC boosts clinical variant interpretation in rare diseases

Nature Reviews Genetics ◽

10.1038/nrg.2016.121 ◽

2016 ◽

Vol 17 (10) ◽

pp. 584-584 ◽

Cited By ~ 17

Author(s):

Orli G. Bahcall

Keyword(s):

Rare Diseases ◽

Variant Interpretation ◽

Clinical Variant

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Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arghavan Bahadorinejad ◽

Ivan Ivanov ◽

Johanna W Lampe ◽

Meredith AJ Hullar ◽

Robert S Chapkin ◽

...

Keyword(s):

16S Rrna ◽

Sample Size ◽

Microbial Communities ◽

State Of The Art ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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CAMISIM: Simulating metagenomes and microbial communities

10.1101/300970 ◽

2018 ◽

Cited By ~ 4

Author(s):

Adrian Fritz ◽

Peter Hofmann ◽

Stephan Majda ◽

Eik Dahms ◽

Johannes Dröge ◽

...

Keyword(s):

Microbial Communities ◽

De Novo ◽

Real Data ◽

Small Data ◽

Data Sets ◽

Sequencing Data ◽

Taxonomic Profiling ◽

Benchmark Data ◽

Sequencing Technologies ◽

Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

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Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz096 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1581-1595 ◽

Cited By ~ 6

Author(s):

Xinlei Zhao ◽

Shuang Wu ◽

Nan Fang ◽

Xiao Sun ◽

Jue Fan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Reference Data ◽

Predictive Accuracy ◽

Cell Types ◽

Superior Performance ◽

Marker Genes ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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VikNGS: A C ++ Variant Integration Kit for Next Generation Sequencing Association Analysis

Bioinformatics ◽

10.1093/bioinformatics/btz716 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zeynep Baskurt ◽

Scott Mastromatteo ◽

Jiafen Gong ◽

Richard F Wintle ◽

Stephen W Scherer ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Association ◽

Association Analysis ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Data Sets ◽

Next Generation ◽

Sequencing Data ◽

Combining Data ◽

Generation Sequencing

Abstract Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools. Availability The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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