scholarly journals aMatReader: Importing adjacency matrices via Cytoscape Automation

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 823
Author(s):  
Brett Settle ◽  
David Otasek ◽  
John H Morris ◽  
Barry Demchak

Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into Cytoscape, where each file represents an edge attribute in a network. Our goal was to import the diverse adjacency matrix formats produced by existing scripts and libraries written in R, MATLAB, and Python, and facilitate importing that data into Cytoscape. To accelerate the import process, aMatReader attempts to predict matrix import parameters by analyzing the first two lines of the file. We also exposed CyREST endpoints to allow researchers to import network matrix data directly into Cytoscape from their language of choice. Many analysis tools deal with networks in the form of an adjacency matrix, and exposing the aMatReader API to automation users enables scripts to transfer those networks directly into Cytoscape with little effort.

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 823 ◽  
Author(s):  
Brett Settle ◽  
David Otasek ◽  
John H Morris ◽  
Barry Demchak

Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into Cytoscape, where each file represents an edge attribute in a network. Our goal was to import the diverse adjacency matrix formats produced by existing scripts and libraries written in R, MATLAB, and Python, and facilitate importing that data into Cytoscape. To accelerate the import process, aMatReader attempts to predict matrix import parameters by analyzing the first two lines of the file. We also exposed CyREST endpoints to allow researchers to import network matrix data directly into Cytoscape from their language of choice. Many analysis tools deal with networks in the form of an adjacency matrix, and exposing the aMatReader API to automation users enables scripts to transfer those networks directly into Cytoscape with little effort.


Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1522
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang

The power method is commonly applied to compute the Perron vector of large adjacency matrices. Blondel et al. [SIAM Rev. 46, 2004] investigated its performance when the adjacency matrix has multiple eigenvalues of the same magnitude. It is well known that the Lanczos method typically requires fewer iterations than the power method to determine eigenvectors with the desired accuracy. However, the Lanczos method demands more computer storage, which may make it impractical to apply to very large problems. The present paper adapts the analysis by Blondel et al. to the Lanczos and restarted Lanczos methods. The restarted methods are found to yield fast convergence and to require less computer storage than the Lanczos method. Computed examples illustrate the theory presented. Applications of the Arnoldi method are also discussed.


2020 ◽  
Author(s):  
Diogo Borges Lima ◽  
Ying Zhu ◽  
Fan Liu

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.


2009 ◽  
Vol 51 (1) ◽  
pp. 71-81 ◽  
Author(s):  
JONATHAN JORDAN

AbstractWe investigate the spectral properties of matrices associated with comb graphs. We show that the adjacency matrices and adjacency matrix Laplacians of the sequences of graphs show a spectral similarity relationship in the sense of work by L. Malozemov and A. Teplyaev (Self-similarity, operators and dynamics,Math. Phys. Anal. Geometry6(2003), 201–218), and hence these sequences of graphs show a spectral decimation property similar to that of the Laplacians of the Sierpiński gasket graph and other fractal graphs.


2018 ◽  
Author(s):  
Natalie Ring ◽  
Jonathan Abrahams ◽  
Miten Jain ◽  
Hugh Olsen ◽  
Andrew Preston ◽  
...  

ABSTRACTThe genome of Bordetella pertussis is complex, with high GC content and many repeats, each longer than 1,000 bp. Short-read DNA sequencing is unable to resolve the structure of the genome; however, long-read sequencing offers the opportunity to produce single-contig B. pertussis assemblies using sequencing reads which are longer than the repetitive sections. We used an R9.4 MinION flow cell and barcoding to sequence five B. pertussis strains in a single sequencing run. We then trialled combinations of the many nanopore-user-community-built long-read analysis tools to establish the current optimal assembly pipeline for B. pertussis genome sequences. Our best long-read-only assemblies were produced by Canu read correction followed by assembly with Flye and polishing with Nanopolish, whilst the best hybrids (using nanopore and Illumina reads together) were produced by Canu correction followed by Unicycler. This pipeline produced closed genome sequences for four strains, revealing inter-strain genomic rearrangement. However, read mapping to the Tohama I reference genome suggests that the remaining strain contains an ultra-long duplicated region (over 100 kbp), which was not resolved by our pipeline. We have therefore demonstrated the ability to resolve the structure of several B. pertussis strains per single barcoded nanopore flow cell, but the genomes with highest complexity (e.g. very large duplicated regions) remain only partially resolved using the standard library preparation and will require an alternative library preparation method. For full strain characterisation, we recommend hybrid assembly of long and short reads together; for comparison of genome arrangement, assembly using long reads alone is sufficient.DATA SUMMARYFinal sequence read files (fastq) for all 5 strains have been deposited in the SRA, BioProject PRJNA478201, accession numbers SAMN09500966, SAMN09500967, SAMN09500968, SAMN09500969, SAMN09500970A full list of accession numbers for Illumina sequence reads is available in Table S1Assembly tests, basecalled read sets and reference materials are available from figshare: https://figshare.com/projects/Resolving_the_complex_Bordetella_pertussis_genome_using_barcoded_nanopore_sequencing/31313Genome sequences for B. pertussis strains UK36, UK38, UK39, UK48 and UK76 have been deposited in GenBank; accession numbers: CP031289, CP031112, CP031113, QRAX00000000, CP031114Source code and full commands used are available from Github: https://github.com/nataliering/Resolving-the-complex-Bordetella-pertussis-genome-using-barcoded-nanopore-sequencingIMPACT STATEMENTOver the past two decades, whole genome sequencing has allowed us to understand microbial pathogenicity and evolution on an unprecedented level. However, repetitive regions, like those found throughout the B. pertussis genome, have confounded our ability to resolve complex genomes using short-read sequencing technologies alone. To produce closed B. pertussis genome sequences it is necessary to use a sequencing technology which can generate reads longer than these problematic genomic regions. Using barcoded nanopore sequencing, we show that multiple B. pertussis genomes can be resolved per flow cell. Use of our assembly pipeline to resolve further B. pertussis genomes will advance understanding of how genome-level differences affect the phenotypes of strains which appear monomorphic at nucleotide-level.This work expands the recently emergent theme that even the most complex genomes can be resolved with sufficiently long sequencing reads. Additionally, we utilise a more widely accessible alternative sequencing platform to the Pacific Biosciences platform already used by large research centres such as the CDC. Our optimisation process, moreover, shows that the analysis tools favoured by the sequencing community do not necessarily produce the most accurate assemblies for all organisms; pipeline optimisation may therefore be beneficial in studies of unusually complex genomes.


2021 ◽  
Author(s):  
Soohyun Lee ◽  
Carl Vitzthum ◽  
Burak H. Alver ◽  
Peter J. Park

AbstractSummaryAs the amount of three-dimensional chromosomal interaction data continues to increase, storing and accessing such data efficiently becomes paramount. We introduce Pairs, a block-compressed text file format for storing paired genomic coordinates from Hi-C data, and Pairix, an open-source C application to index and query Pairs files. Pairix (also available in Python and R) extends the functionalities of Tabix to paired coordinates data. We have also developed PairsQC, a collapsible HTML quality control report generator for Pairs files.AvailabilityThe format specification and source code are available at https://github.com/4dn-dcic/pairix, https://github.com/4dn-dcic/Rpairix and https://github.com/4dn-dcic/[email protected] or [email protected]


2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Fatih Yılmaz ◽  
Durmuş Bozkurt

Recently there is huge interest in graph theory and intensive study on computing integer powers of matrices. In this paper, we consider one type of directed graph. Then we obtain a general form of the adjacency matrices of the graph. By using the well-known property which states the(i,j)entry ofAm(Ais adjacency matrix) is equal to the number of walks of lengthmfrom vertexito vertexj, we show that elements ofmth positive integer power of the adjacency matrix correspond to well-known Jacobsthal numbers. As a consequence, we give a Cassini-like formula for Jacobsthal numbers. We also give a matrix whose permanents are Jacobsthal numbers.


Author(s):  
Duanling Li ◽  
Chunxia Li ◽  
Zhonghai Zhang ◽  
Xianwen Kong

Metamorphic transformation is a fundamental and key issue in the design and analysis of metamorphic mechanisms. It is tedious to represent and calculate the metamorphic transformations of metamorphic parallel mechanisms using the existing adjacency matrix method. To simplify the configuration transformation analysis, we propose a new method based on block adjacency matrix to analyze the configuration transformations of metamorphic parallel mechanisms. A block adjacency matrix is composed of three types of elements, including limb matrices that are adjacency matrices each representing a limb of a metamorphic parallel mechanism, row matrices each representing how a limb is connected to the moving platform, and column matrices each representing how a limb is connected to the base. Manipulations of the block adjacency matrix for analyzing the metamorphic transformations are presented systematically. If only the internal configuration of a limb changes, the configuration transformations can be obtained by simply calculating the corresponding limb matrix. A 3-URRRR metamorphic parallel mechanism, which has five configurations including a 1-DOF translation configuration and a 3-DOF spherical motion configuration, is taken as an example to illustrate the effectiveness of the proposed approach to the metamorphic transformation analysis of metamorphic parallel mechanism.


PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5858
Author(s):  
Sobia Idrees ◽  
Åsa Pérez-Bercoff ◽  
Richard J. Edwards

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.


2017 ◽  
Author(s):  
Luke Zappia ◽  
Belinda Phipson ◽  
Alicia Oshlack

AbstractAs single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records of the growth of the field over time.Author summaryIn recent years single-cell RNA-sequeing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focued on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and preprint publications.


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