BOL: Related items

homologizer: Phylogenetic phasing of gene copies into polyploid subgenomes

Abhi — Sat, 03 Jun 2023 19:19:10 -0500

This tutorial describes the usage of homologizer to phase gene copies into polyploid subgenomes. The tutorial is an abbreviated version of a soon-to-be published paper in Methods in Molecular Biology. Please see that paper for many more details and practical considerations for running homologizer analyses. If you use homologizer, please cite the paper in which we first describe the method:

Freyman, W.A., Johnson, M.G., and C.J. Rothfels. 2022. Homologizer: phylogenetic phasing of gene copies into polyploid subgenomes. bioRxiv 2020.10.22.351486v4

homologizer is implemented in RevBayes. Please see http://revbayes.com to download and install RevBayes. For users without previous RevBayes experience, we recommend the tutorials at http://revbayes.com.

Address of the bookmark: https://github.com/wf8/homologizer

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

Jit — Thu, 15 Feb 2018 10:21:02 -0600

Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the k-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10⁹ bp).
Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.

A manual can be found here.

Address of the bookmark: https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html

FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads

Jit — Tue, 28 Jan 2020 03:27:33 -0600

FastGT is a program package for whole-genome genotyping of genome variants directly from raw sequencing reads. It is written in C and runs in Linux. FastGT uses a list of variant-specific k-mer pairs that are unique in human genome, counts the frequency of k-mers in sequencing data and predicts the genotype. All this takes less than 1 hour on average low-cost Linux server.

http://bioinfo.ut.ee/FastGT/

https://github.com/bioinfo-ut/GenomeTester4/

Address of the bookmark: http://bioinfo.ut.ee/FastGT/

Method in Comparative genomics !!

Jit — Wed, 09 Nov 2016 16:29:24 -0600

We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change.

We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.

Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf

SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins

BioStar — Sat, 06 Jul 2024 04:29:16 -0500

SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:

Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
Co-assembly of a large number of metagenomes via merging of individual metagenomes
Includes binning and bin checking, for retrieving individual genomes
The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

ChromHMM: Chromatin state discovery and characterization

Abhimanyu Singh — Wed, 19 Apr 2017 04:06:23 -0500

ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

Address of the bookmark: http://compbio.mit.edu/ChromHMM/

Breakpointer: using local mapping artifacts to support sequence breakpoint discovery from single-end reads

Jit — Tue, 12 Jun 2018 12:41:10 -0500

Breakpointer is a fast tool for locating sequence breakpoints from the alignment of single end reads (SE) produced by next generation sequencing (NGS). It adopts a heuristic method in searching for local mapping signatures created by insertion/deletions (indels) or more complex structural variants(SVs).

Address of the bookmark: https://github.com/ruping/Breakpointer

Boku Chair of Bioinformatics

Sun, 14 Jul 2013 12:37:23 -0500

The Bioinformatics group at Boku University has two main areas of interest, underpinning a common goal, the study of complex systems in living organisms. To overcome the engineered redundancies and combinatorial effects prevalent in higher eukaryotes, novel views augmenting the classical gene by gene approaches are required. We combine
Work to establish improved quantitative experimental assays (such as microarrays or differential in-gel electrophoresis) and
Development of modern computational methods (such as hierarchical probabilistic models or integration of heterogeneous data sources)

Link @ http://bioinf.boku.ac.at/

List of gene ontology software and tools

Jit — Sun, 09 Mar 2014 14:48:19 -0500

The Gene Ontology (GO) is a set of associations from biological phrases to specific genes that are either chosen by trained curators or generated automatically. GO is designed to rigorously encapsulate the known relationships between biological terms and and all genes that are instances of these terms. These Gene Ontology has become an extremely useful tool for the analysis of genomic data and structuring of biological knowledge. Several excellent software tools for navigating the gene ontology have been developed.

The GO provides core biological knowledge representation for modern biologists, whether computationally or experimentally based. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Although extensively used in data analysis workflows, and widely incorporated into numerous data analysis platforms and applications, the general user of GO resources often misses fundamental distinctions about GO structures, GO annotations, and what can and can not be extrapolated from GO resources. Here are ten quick tips for using the Gene Ontology.

Read "Ten Quick Tips for Using the Gene Ontology" at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003343

Following are the most commonly used old and new GO term enrichment determination tools. These tools are recommended to people working in a wet-lab.

CLASSIFI (Department of Pathology, UT Southwestern Medical Center)

CLASSIFI (Cluster Assignment for Biological Inference) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene OntologyTM (GO) gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.

http://pathcuric1.swmed.edu/pathdb/classifi.html

EasyGO (China Agricultural University)

EasyGO is designed to automate enrichment job for experimental biologists to identify enriched Gene Ontology (GO) terms in a list of microarray probe sets or gene identifiers (with expression information for PAGE analysis). Also EasyGO is also a GO annotation database, especially focus on agronomical species, supporting 30 species. It is user friendly, with advanced result browsing format and in-time update.

http://bioinformatics.cau.edu.cn/neweasygo/

http://bioinformatics.cau.edu.cn/easygo/

g:GOSt (Institute of Computer Science, University of Tartu)

g:GOSt retrieves most significant Gene Ontology (GO) terms, KEGG and REACTOME pathways, and TRANSFAC motifs to a user-specified group of genes, proteins or microarray probes. g:GOSt also allows analysis of ranked or ordered lists of genes, visual browsing of GO graph structure, interactive visualisation of retrieved results, and many other features. Multiple testing corrections are applied to extract only statistically important results.

http://biit.cs.ut.ee/gprofiler/

DAVID : Gene Functional Classification (Laboratory of Immunopathogenesis and Bioinformatics, NIAID)

The Functional Classification Tool provides a rapid means to organize large lists of genes into functionally related groups to help unravel the biological content captured by high throughput technologies.

http://david.abcc.ncifcrf.gov/gene2gene.jsp

http://david.abcc.ncifcrf.gov/

API https://github.com/chrisamiller/davidapi

GOEAST (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences)

GOEAST is web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.

http://omicslab.genetics.ac.cn/GOEAST/

GOstat (Walter and Eliza Hall Institute of Medical Research)

Find statistically overrepresented GO terms within a group of genes

http://gostat.wehi.edu.au/

GOrilla (Technion - Laboratory of Computational Biology , Israel Institute of Technology)

GOrilla is a tool for identifying and visualizing enriched GO terms in ranked lists of genes.
It uses two approaches, first by searching for enriched GO terms that appear densely at the top of a ranked list of genes or by searching for enriched GO terms in a target list of genes compared to a background list of genes.

GOrilla makes nice pictures !!!!

http://cbl-gorilla.cs.technion.ac.il/

Gene Ontology for Functional Analysis (GOFFA)

GOFFA is a tool developed for ArrayTrack™ that takes a list of genes and identifies terms in Gene Ontology (GO) disclaimer icon associated with those genes.

It provides several tools to view/access the GO term hierarchy, full listing of GO terms annotated with the genes associated with a given term with statically useful report.

http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm233315.htm

GOAT (The University of Manchester)

The aim of the GOAT project is to create an application that will guide users, especially biomedical researchers, in the annotation of gene products with terms from the Gene Ontology.

http://goat.man.ac.uk/

Script https://github.com/tanghaibao/goatools/

REVIGO ( Rudjer Boskovic Institute, Croatia)

REViGO is a web server that can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.

http://revigo.irb.hr/

QuickGo (EMBL-EBI Institute)

It uses extensive computational filters to allow the generation of specific subsets of GO annotations, mapped to sequence identifiers of your choice. Then GO slims are used which is collective list of GO full set of terms available from the Gene Ontology project.

http://www.ebi.ac.uk/QuickGO/

GOLEM

An interactive graph-based gene-ontology navigation and analysis tool. GOLEM is a userful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy.

http://reducio.princeton.edu/GOLEM/

BGI Web Gene Ontology (WEGO) Annotation Plot (Beijing Genomics Institute)

WEGO () is a useful tool for plotting GO annotation results. It has been widely used in many important biological research projects, such as the rice genome project [Yu, J. et al. Science 296, 79-92 (2002); Yu, J. et al. PLoS Biol 3, e38 (2005)] and the silkworm genome project [Xia, Q. et al. Science 306, 1937-40 (2004)]. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO along with two other tools, namely External to GO Query and GO Archive Query, are freely available for all users. Any suggestions are welcome at wego@genomics.org.cn. Here is a sample output generated by WEGO

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

GeneGO MetaCore (MIT)

GeneGo is a leading provider of data mining & analysis solutions in systems biology. MetaCore, GeneGo's flapship product, is an integrated software suite for functional analysis of experimental data. MetaCore is based on a curated database of human protein-protein, protein-DNA interactions, transcription factors, signaling and metabolic pathways, disease and toxicity, and the effects of bioactive molecules.

https://portal.genego.com/

GOEx (Stony Brook University)

GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics.

http://pcarvalho.com/patternlab

GOssTo

GOssTo and GOssToWeb are tools to calculate the semantic similarity between genes or terms in the Gene Ontology.

http://www.paccanarolab.org/gosstoweb/

GO Workbench

The Gene Ontology Analysis Viewer allows direct browsing of the Gene Ontology, and also the visualization of GO Term analysis results.

http://wiki.c2b2.columbia.edu/workbench/index.php/Gene_Ontology_Viewer

Some other useful list of GO software and tools is available at http://www.geneontology.org/GO.tools.shtml#browser

Yet another useful webpage with list of GO tools at http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools