HaploGrouper: a generalized approach to haplogroup classification

2020 ◽  
Author(s):  
Anuradha Jagadeesan ◽  
S Sunna Ebenesersdóttir ◽  
Valdis B Guðmundsdóttir ◽  
Elisabet Linda Thordardottir ◽  
Kristjan H S Moore ◽  
...  
Keyword(s):  
Mitochondrial Dna ◽  
Phylogenetic Tree ◽  
Y Chromosome ◽  
State Of The Art ◽  
Supplementary Information ◽  
Sequence Variants ◽  
Use Case ◽  
Supplementary Data ◽  
Human Mitochondrial Dna ◽  
Comparable Accuracy

Abstract Motivation We introduce HaploGrouper, a versatile software to classify haplotypes into haplogroups on the basis of a known phylogenetic tree. A typical use case for this software is the assignment of haplogroups to human mitochondrial DNA (mtDNA) or Y-chromosome haplotypes. Existing state-of-the-art haplogroup-calling software is typically hard-wired to work only with either mtDNA or Y-chromosome haplotypes from humans. Results HaploGrouper exhibits comparable accuracy in these instances and has the advantage of being able to assign haplogroups to any kind of haplotypes from any species—given an extant annotated phylogenetic tree defined by sequence variants. Availability and implementation The software is available at the following URL https://gitlab.com/bio_anth_decode/haploGrouper. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

2020 ◽  
Author(s):  
Andrew E Teschendorff ◽  
Alok K Maity ◽  
Xue Hu ◽  
Chen Weiyan ◽  
Matthias Lechner
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Computational Cost ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Current State ◽  
Multipotent Cells ◽  
Comparable Accuracy

Abstract Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CorGAT: a tool for the functional annotation of SARS-CoV-2 genomes

2020 ◽  
Author(s):  
Matteo Chiara ◽  
Federico Zambelli ◽  
Marco Antonio Tangaro ◽  
Pietro Mandreoli ◽  
David S Horner ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Ad Hoc ◽  
State Of The Art ◽  
Supplementary Information ◽  
Genomic Sequences ◽  
Supplementary Data ◽  
Evolutionary Patterns ◽  
Genomic Variants ◽  
Art Methods ◽  
Available Resources

Abstract Summary While over 200 000 genomic sequences are currently available through dedicated repositories, ad hoc methods for the functional annotation of SARS-CoV-2 genomes do not harness all currently available resources for the annotation of functionally relevant genomic sites. Here, we present CorGAT, a novel tool for the functional annotation of SARS-CoV-2 genomic variants. By comparisons with other state of the art methods we demonstrate that, by providing a more comprehensive and rich annotation, our method can facilitate the identification of evolutionary patterns in the genome of SARS-CoV-2. Availabilityand implementation Galaxy   http://corgat.cloud.ba.infn.it/galaxy; software: https://github.com/matteo14c/CorGAT/tree/Revision_V1; docker: https://hub.docker.com/r/laniakeacloud/galaxy_corgat. Supplementary information Supplementary data are available at Bioinformatics online.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

2021 ◽  
Author(s):  
Ting-Hsuan Wang ◽  
Cheng-Ching Huang ◽  
Jui-Hung Hung
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
A Priori ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Comparable Accuracy ◽  
Meta Analyses ◽  
Next Generation Sequencing Ngs ◽  
Adapter Trimming ◽  
Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CPS analysis: self-contained validation of biomedical data clustering

2020 ◽  
Vol 36 (11) ◽  
pp. 3516-3521 ◽  
Author(s):  
Lixiang Zhang ◽  
Lin Lin ◽  
Jia Li
Keyword(s):  
Data Clustering ◽  
State Of The Art ◽  
R Package ◽  
Research Community ◽  
Supplementary Information ◽  
Biomedical Data ◽  
Data Generation ◽  
Supplementary Data ◽  
Point Set ◽  
Class Labels

Abstract Motivation Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. Results We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. Availability and implementation The method is implemented in an R package called OTclust, available on CRAN. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation

2009 ◽  
Vol 30 (2) ◽  
pp. E386-E394 ◽  
Author(s):  
Mannis van Oven ◽  
Manfred Kayser
Keyword(s):  
Mitochondrial Dna ◽  
Phylogenetic Tree ◽  
Human Mitochondrial Dna ◽  
Dna Variation

scholarly journals Temporal network alignment via GoT-WAVE

2019 ◽  
Vol 35 (18) ◽  
pp. 3527-3529 ◽  
Author(s):  
David Aparício ◽  
Pedro Ribeiro ◽  
Tijana Milenković ◽  
Fernando Silva
Keyword(s):  
User Interface ◽  
State Of The Art ◽  
Source Code ◽  
Network Alignment ◽  
Supplementary Information ◽  
Temporal Network ◽  
Temporal Networks ◽  
Supplementary Data ◽  
Node Similarity ◽  
User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PATO: Pangenome Analysis Toolkit

2021 ◽  
Author(s):  
Miguel D. Fernández-de-Bobadilla ◽  
Alba Talavera-Rodríguez ◽  
Lucía Chacón ◽  
Fernando Baquero ◽  
Teresa M. Coque ◽  
...  
Keyword(s):  
Population Structure ◽  
Statistical Analysis ◽  
Core Genome ◽  
State Of The Art ◽  
Source Code ◽  
Supplementary Information ◽  
Complete Analysis ◽  
Large Set ◽  
Supplementary Data ◽  
Desktop Computer

AbstractMotivationComparative genomics is a growing field but one that will be eventually overtaken by sample size studies and the increase of available genomes in public databases. We present the Pangenome Analysis Toolkit (PATO) designed to simultaneously analyze thousands of genomes using a desktop computer. The tool performs common tasks of pangenome analysis such as core-genome definition and accessory genome properties and includes new features that help characterize population structure, annotate pathogenic features and create gene sharedness networks. PATO has been developed in R to integrate with the large set of tools available for genetic, phylogenetic and statistical analysis in this environment.ResultsPATO can perform the most demanding bioinformatic analyses in minutes with an accuracy comparable to state-of-the-art software but 20–30x times faster. PATO also integrates all the necessary functions for the complete analysis of the most common objectives in microbiology studies. Lastly, PATO includes the necessary tools for visualizing the results and can be integrated with other analytical packages available in R.AvailabilityThe source code for PATO is freely available at https://github.com/irycisBioinfo/PATO under the GPLv3 [email protected] informationSupplementary data are available at Bioinformatics online

scholarly journals QPARSE: searching for long-looped or multimeric G-quadruplexes potentially distinctive and druggable

2019 ◽  
Author(s):  
Michele Berselli ◽  
Enrico Lavezzo ◽  
Stefano Toppo
Keyword(s):  
Human Gene ◽  
State Of The Art ◽  
Comprehensive Analysis ◽  
Supplementary Information ◽  
Gene Promoters ◽  
Supplementary Data ◽  
Stem Loop ◽  
Hiv 1 ◽  
Rna And Dna ◽  
The Web

Abstract Motivation G-quadruplexes (G4s) are non-canonical nucleic acid conformations that are widespread in all kingdoms of life and are emerging as important regulators both in RNA and DNA. Recently, two new higher-order architectures have been reported: adjacent interacting G4s, and G4s with stable long loops forming stem-loop structures. As there are no specialized tools to identify these conformations, we developed QPARSE. Results QPARSE can exhaustively search for degenerate potential quadruplex-forming sequences (PQSs) containing bulges and/or mismatches at genomic level, as well as either multimeric or long-looped PQS (MPQS and LLPQS respectively). While its assessment vs. known reference datasets is comparable with the state-of-the-art, what is more interesting is its performance in the identification of MPQS and LLPQS that present algorithms are not designed to search for. We report a comprehensive analysis of MPQS in human gene promoters and the analysis of LLPQS on three experimentally validated case studies from HIV-1, BCL2, and hTERT. Availability QPARSE is freely accessible on the web at http://www.medcomp.medicina.unipd.it/qparse/index or downloadable from github as a python 2.7 program https://github.com/B3rse/qparse Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FQSqueezer: k-mer-based compression of sequencing data

2019 ◽  
Author(s):  
Sebastian Deorowicz
Keyword(s):  
Data Compression ◽  
State Of The Art ◽  
Genomic Data ◽  
General Purpose ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Partial Matching ◽  
Supplementary Material ◽  
Better Than

AbstractMotivationThe amount of genomic data that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce quite large archives.ResultsWe present FQSqueezer, a novel compression algorithm for sequencing data able to process single- and paired-end reads of variable lengths. It is based on the ideas from the famous prediction by partial matching and dynamic Markov coder algorithms known from the general-purpose-compressors world. The compression ratios are often tens of percent better than offered by the state-of-the-art tools.Availability and Implementationhttps://github.com/refresh-bio/[email protected] informationSupplementary data are available at publisher’s Web site.

scholarly journals MolRep: A Deep Representation Learning Library for Molecular Property Prediction

2021 ◽  
Author(s):  
Jiahua Rao ◽  
Shuangjia Zheng ◽  
Ying Song ◽  
Jianwen Chen ◽  
Chengtao Li ◽  
...  
Keyword(s):  
State Of The Art ◽  
Source Code ◽  
Representation Learning ◽  
Supplementary Information ◽  
Data Sets ◽  
Supplementary Data ◽  
Property Prediction ◽  
Average Rank ◽  
Benchmark Data ◽  
Classification Tasks

AbstractSummaryRecently, novel representation learning algorithms have shown potential for predicting molecular properties. However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible. Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. Furthermore, we ran more than 12.5 million experiments to optimize hyperparameters for each method on 12 common benchmark data sets. As a result, CMPNN achieves the best results ranked the 1st in 5 out of 12 tasks with an average rank of 1.75. Relatively, ECC has good performance in classification tasks and MAT good for regression (both ranked 1st for 3 tasks) with an average rank of 2.71 and 2.6, respectively.AvailabilityThe source code is available at: https://github.com/biomed-AI/MolRepSupplementary informationSupplementary data are available online.

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