Serpentine: a flexible 2D binning method for differential Hi-C analysis

Abstract Motivation Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation. Results We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses. Availability and implementation Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa274 ◽

2020 ◽

Vol 36 (13) ◽

pp. 4097-4098 ◽

Cited By ~ 3

Author(s):

Anna Breit ◽

Simon Ott ◽

Asan Agibetov ◽

Matthias Samwald

Keyword(s):

Link Prediction ◽

Large Scale ◽

Source Code ◽

Machine Learning Algorithms ◽

Knowledge Networks ◽

Supplementary Information ◽

Supplementary Data ◽

Biomedical Knowledge ◽

High Quality ◽

Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra

Bioinformatics ◽

10.1093/bioinformatics/btz085 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3489-3490 ◽

Cited By ~ 1

Author(s):

Diogo B Lima ◽

André R F Silva ◽

Mathieu Dupré ◽

Marlon D M Santos ◽

Milan A Clasen ◽

...

Keyword(s):

Quality Control ◽

Mass Spectra ◽

Rate Increase ◽

Supplementary Information ◽

Supplementary Data ◽

Top Down ◽

High Quality ◽

Garbage Collector ◽

E Coli ◽

Spectral Libraries

Abstract Motivation We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates. Results We demonstrate that a twofold rate increase for two E. coli top-down proteomics datasets may be achievable. Availability and implementation http://patternlabforproteomics.org/tdgc, freely available for academic use. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Accurate detection of short and long active ORFs using Ribo-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz878 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2053-2059 ◽

Cited By ~ 3

Author(s):

Saket Choudhary ◽

Wenzheng Li ◽

Andrew D. Smith

Keyword(s):

Caenorhabditis Elegans ◽

Deep Sequencing ◽

Open Reading Frames ◽

Supplementary Information ◽

Supplementary Data ◽

Accurate Detection ◽

Short Orfs ◽

Coding Potential ◽

Reading Frames

Abstract Motivation Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise. Results We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish. Availability and implementation Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Bringing data from curated pathway resources to Cytoscape with OmniPath

Bioinformatics ◽

10.1093/bioinformatics/btz968 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2632-2633 ◽

Cited By ~ 6

Author(s):

Francesco Ceccarelli ◽

Denes Turei ◽

Attila Gabor ◽

Julio Saez-Rodriguez

Keyword(s):

Source Code ◽

Large Body ◽

Supplementary Information ◽

Supplementary Data ◽

Network Resources ◽

High Quality ◽

Comprehensive Collection ◽

Intuitive Interface ◽

Growing Network

Abstract Summary Multiple databases provide valuable information about curated pathways and other resources that can be used to build and analyze networks. OmniPath combines 61 (and continuously growing) network resources into a comprehensive collection, with over 120 000 interactions. We present here the OmniPath App, a Cytoscape plugin to flexibly import data from OmniPath via a simple and intuitive interface. Thus, it makes possible to directly access the large body of high-quality knowledge provided by OmniPath within Cytoscape for inspection and further use with other tools. Availability and implementation The OmniPath App has been developed for Cytoscape 3 in the Java programing language. The latest source code and the plugin can be found at: https://github.com/saezlab/Omnipath_Cytoscape and http://apps.cytoscape.org/apps/omnipath, respectively. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps

Bioinformatics ◽

10.1093/bioinformatics/bty1059 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2724-2729 ◽

Cited By ~ 3

Author(s):

L Carron ◽

J B Morlot ◽

V Matthys ◽

A Lesne ◽

J Mozziconacci

Keyword(s):

Long Range ◽

Short Range ◽

Supplementary Information ◽

Supplementary Data ◽

Missing Information ◽

High Confidence ◽

Contact Maps ◽

Genome Wide ◽

Algorithmic Procedure

Abstract Motivation Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. Results We propose to use the sparse information contained in raw contact maps to infer high-confidence contact counts between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. Availability and implementation Boost-HiC is available at https://github.com/LeopoldC/Boost-HiC. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

LRez: C ++ API and toolkit for analyzing and managing Linked-Reads data

Bioinformatics Advances ◽

10.1093/bioadv/vbab022 ◽

2021 ◽

Author(s):

Pierre Morisse ◽

Claire Lemaitre ◽

Fabrice Legeai

Keyword(s):

Genome Assembly ◽

Low Cost ◽

Variant Calling ◽

Supplementary Information ◽

Supplementary Data ◽

High Quality ◽

Dna Molecule ◽

Sequencing Technologies ◽

Wide Range ◽

Genomic Regions

Abstract Motivation Linked-Reads technologies combine both the high-quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. Results We introduce LRez, a C ++ API and toolkit which allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. Availability and implementation LRez is implemented in C ++, supported on Unix-based platforms, and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary information Supplementary data are available at Bioinformatics Advances

Download Full-text

Reconstructing signaling pathways using regular language constrained paths

Bioinformatics ◽

10.1093/bioinformatics/btz360 ◽

2019 ◽

Vol 35 (14) ◽

pp. i624-i633 ◽

Cited By ~ 3

Author(s):

Mitchell J Wagner ◽

Aditya Pratapa ◽

T M Murali

Keyword(s):

Signaling Pathways ◽

Regular Language ◽

Interaction Network ◽

Automated Analysis ◽

Supplementary Information ◽

Natural Question ◽

Supplementary Data ◽

High Quality ◽

Alternative Approaches ◽

New Interactions

Abstract Motivation High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway. Results We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry. Availability and implementation https://github.com/Murali-group/RegLinker. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

CATHER: a novel threading algorithm with predicted contacts

Bioinformatics ◽

10.1093/bioinformatics/btz876 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2119-2125 ◽

Cited By ~ 1

Author(s):

Zongyang Du ◽

Shuo Pan ◽

Qi Wu ◽

Zhenling Peng ◽

Jianyi Yang

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Supplementary Information ◽

Supplementary Data ◽

Contact Map ◽

Test Set ◽

Benchmark Tests ◽

Independent Test ◽

Push Forward

Abstract Motivation Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts. Results We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts. Availability and implementation http://yanglab.nankai.edu.cn/CATHER/. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

ConPlot: web-based application for the visualization of protein contact maps integrated with other data

Bioinformatics ◽

10.1093/bioinformatics/btab049 ◽

2021 ◽

Author(s):

Filomeno Sánchez Rodríguez ◽

Shahram Mesdaghi ◽

Adam J Simpkin ◽

J Javier Burgos-Mármol ◽

David L Murphy ◽

...

Keyword(s):

Empty Space ◽

Structural Features ◽

Supplementary Information ◽

Contact Map ◽

Web Based ◽

Contact Maps ◽

File Formats ◽

Residue Contacts ◽

Contact Data ◽

Protein Contact Maps

Abstract Summary Covariance-based predictions of residue contacts and inter-residue distances are an increasingly popular data type in protein bioinformatics. Here we present ConPlot, a web-based application for convenient display and analysis of contact maps and distograms. Integration of predicted contact data with other predictions is often required to facilitate inference of structural features. ConPlot can therefore use the empty space near the contact map diagonal to display multiple coloured tracks representing other sequence-based predictions. Popular file formats are natively read and bespoke data can also be flexibly displayed. This novel visualization will enable easier interpretation of predicted contact maps. Availability and implementation available online at www.conplot.org, along with documentation and examples. Alternatively, ConPlot can be installed and used locally using the docker image from the project’s Docker Hub repository. ConPlot is licensed under the BSD 3-Clause. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

10.1101/181842 ◽

2017 ◽

Cited By ~ 4

Author(s):

Oana Ursu ◽

Nathan Boley ◽

Maryna Taranova ◽

Y.X. Rachel Wang ◽

Galip Gurkan Yardimci ◽

...

Keyword(s):

Random Walks ◽

Critical Role ◽

Three Dimensional ◽

Cell Types ◽

Chromosome Conformation Capture ◽

Supplementary Information ◽

Contact Map ◽

Chromosome Conformation ◽

Contact Maps ◽

Map Graphs

AbstractMotivationThe three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.ResultsWe introduce a concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCO’s sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP.AvailabilitySoftware implementing GenomeDISCO is available at https://github.com/kundajelab/[email protected] informationSupplementary data are available at Bioinformatics online.

Download Full-text