<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44601?offset=20 https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources Tue, 28 Jun 2022 04:08:06 -0500 https://bioinformaticsonline.com/blog/view/43896/list-of-comparative-genomics-resources <![CDATA[List of comparative genomics resources !]]>

Compare structural and functional annotations of proteins between sequenced genomes.

View AREs in the human transcriptome and study the comparative genomics of AREs in model organisms.

Find information about orthologous genes in prokaryotes.

Search for publicly available QTL data on livestocks and animal species.

Find information about bovine genomics data.

A multi-organism web-based resource system designed to easily retrieve, correlate and interpret data across species.

A database resource of developmentally associated conserved non-coding elements.

Delineate conserved non-coding blocks from upstream regions of putative orthologous gene pairs from man, mouse, rat, fugu, Mus musculus, Danio rerio, and zebrafish.

Find coexpressed gene lists and networks in human and mouse.

Construct phylogenetic tree of microorganisms based on oligopeptide content of their complete proteomes.

A novel database server that classifies GenBank's dbEST (database of expressed gene sequences) libraries and removes contaminants.

Find information about the ancestral relationship between genes.

Provides an overview of the genomic organization of microRNAs and extent of conservation during evolution in different metazoan species.

Conduct comparative biometric analysis of chromosomes of different organisms.

Search for Indices of gene and transcripts in human and mouse.

Search and compare 12 new and old Drosophila genomes.

Access to whole genome alignments of human, mouse, rat and fish sequences.

Find eukaryotic paralog/paralogon information.

Analyze the evolutionary process of overlapping genes when comparing different species.

The first publicly available system reported to handle inter-species gene mention normalization.

A web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes.

Visualize gene indices of human, mouse, Arabidopsis, Zebrafish, Drosophila and Sheep.

Find information about mutated mouse genes.

A data management and analysis system for metagenomes

Search for influenza sequence, vaccine, and drug resistance information.

LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal models for their research.

The missing link between multi-species full genome comparisons and functional analysis.

Conduct comparative analysis of completely sequenced microbial genomes.

A biologist-centric software for evolutionary analysis of DNA and protein sequences.

Conduct single nucleotide polymorphisms diversity measurements among homologous sequences from the Mammalia class.

Find information about 1000s of microbial genomes.

A database dedicated to the study of genome conservation.

Explore orthologous relations across 352 complete genomes.

Browse complete genomes in several clades.

A database of orthologous genomic markers for placental mammal phylogenetics.

Conduct genome wide functional and structural analysis.

Conduct genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes.

Compare phenotypes of a given gene or gene set in different model organisms.

Phylemon is a web server that integrates a selected suite of more than 20 different tools from the most popular stand-alone programs of phylogenetic and evolutionary analysis.

Use this database to see where in the evolution some phylogenetic lineages were started, and over which species they were contained.

Search for genomic information on nematode satellite species Pristionchus pacificus.

Find information about related protein sequences.

Database hosting genomics and post-genomics data from multiple protozoans.

A database of pseudogene families based on the protein families from the Pfam database.

Find genomic information about mammals.

Find information about predicted regulons in prokaryotic transcription regulation.

Perform systematic comparison of proteome data among species.

A comparative map viewer dedicated to the biology of the Solanaceae family.

Analyze data from functional analysis on fragmented microbial genetic material.

Visualize and explore comparative genomic hybridization data sets.

Search for genome-wide annotations of regulatory sites in yeast and prokaryotes genomes.

Search for information on adaptive evolution in gene families of higher plants and chordate.

Generate graphical maps of circular genomes that show sequence features, base composition plots, analysis results and sequence similarity plots.

Conduct a comprehensive analysis of genes and genomes.

An interactive online poster presentation on the Macaque genome, including high-quality images, video clips, and Web resources

Search for annotated genetic information of expressed sequence tags (ESTs) in different eukaryotic organisms.

Find mapping and expression information for a unigene cluster (ESTs and full-length mRNA sequences organized into clusters that each represent a unique known or putative gene)

A public online resource for identifying or designing 'universal' overgo-hybridization probes from conserved sequences that can be used to efficiently screen one or more genomic libraries from a designated group of species.

Comprehensive suite of programs and databases for comparative analysis of genomic sequences.

Find metabolic networks and phylogenomic information on a taxonomically diverse range of eukaryotes.

]]> Shruti Paniwala https://bioinformaticsonline.com/pages/view/35559/computational-resources-for-te-discovery-and-te-detection Mon, 12 Feb 2018 10:29:18 -0600 https://bioinformaticsonline.com/pages/view/35559/computational-resources-for-te-discovery-and-te-detection <![CDATA[Computational resources for TE discovery and TE detection]]> Transposable Elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis.

Following are the list of resource and location for TE discovery and TE detection:

BLASTER suite http://urgi.versailles.inra.fr/development/blaster/ 

Censor http://www.girinst.org/censor/download.php 

find_ltr http://darwin.informatics.indiana.edu/cgi-bin/evolution/ltr.pl 

FINDMITE http://jaketu.biochem.vt.edu/dl_software.htm

HMMER http://hmmer.janelia.org/

LTR_FINDER http://tlife.fudan.edu.cn/ltr_finder/

LTR_STRUC http://www.genetics.uga.edu/retrolab/data/LTR_Struc.html

LTR_MINER http://genomebiology.com/2004/5/10/R79/suppl/s7

LTR_par http://www.eecs.wsu.edu/~ananth/software.htm

MAK http://wesslercluster.plantbio.uga.edu/mak06.html

MaskerAid http://blast.wustl.edu/maskeraid/

mer-engine http://mer-engine.cshl.edu/mer-home.php

mreps http://bioinfo.lifl.fr/mreps/

PILER http://www.drive5.com/piler/

PLOTREP http://repeats.abc.hu/cgi-bin/plotrep.pl

RepBase http://www.girinst.org/

RepeatFinder http://cbcb.umd.edu/software/RepeatFinder/

RepeatGluer http://nbcr.sdsc.edu/euler/intro_tmp.htm

RepeatMasker http://www.repeatmasker.org/

RepeatRunner http://www.yandell-lab.org/repeat_runner/index.html

RepeatScout http://repeatscout.bioprojects.org/

repeat-match http://mummer.sourceforge.net/

REPuter http://www.genomes.de/

RetroMap http://www.burchsite.com/bioi/RetroMapHome.html

SMaRTFinder http://bioinf.dimi.uniud.it/software/software/smartfinder

Tandem Repeats Finder http://tandem.bu.edu/trf/trf.html

Transposon Cluster Finder http://www.mssm.edu/labs/warbup01/paper/files.html

TE nest http://www.plantgdb.org/prj/TE_nest/TE_nest.html

TRANSPO http://alggen.lsi.upc.es/recerca/search/transpo/transpo.html

TSDfinder http://www.ncbi.nlm.nih.gov/CBBresearch/Landsman/TSDfinder/

Tu Lab TE tools http://jaketu.biochem.vt.edu/dl_software.htm

WU-BLAST http://blast.wustl.edu

]]> Abhimanyu Singh https://bioinformaticsonline.com/blog/view/43898/online-resources-on-must-read-papers-in-evolutionary-biology-for-a-literature-club Tue, 28 Jun 2022 07:29:08 -0500 https://bioinformaticsonline.com/blog/view/43898/online-resources-on-must-read-papers-in-evolutionary-biology-for-a-literature-club <![CDATA[Online resources on must-read papers in evolutionary biology, for a literature club]]> 1. *Nick Barton:* - The textbook "Evolution" by Nick Barton, with resources for exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J. A., Goldstein, D. B., & Patel, N. H. (2007). Evolution. Cold Spring Harbor Laboratory Press. - Papers from a course named "Classics in Evolutionary Biology": Evolutionary Synthesis 1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York. (esp. Ch. IV). 2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford University Press, Oxford. Selected Sections - Fundamental Theorem. Genetic Variation 1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to the study of genic heterozygosity in natural populations. II. Amount of variation and degree of heterozygosity in natural populations of Drosophila pseudoobscura. Genetics. 54:595-609. 1b. Sachidandam et al. 2001. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. 409: 928-33. 2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural populations VII The allelism of lethals in the third chromosome of Drosophila pseudoobscura. Genetics 27: 363-394. Recombination and evolution 1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits to artificial selection. Genet. Res. 8:269-294. 2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable gene. Genet. Res. 23: 23-35. Understanding sequence variation 1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA polymorphism correlate with recombination rate in Drosophila melanogaster. Nature 356: 519-520. 2. Green R. E., Reich D., Pääbo S., 2010 A draft sequence of the Neandertal genome. Science 328: 710-722. Quantitative Genetics: variation in complex traits 1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495- 512-514- 532-533. 2. Turelli M., 1984 Heritable genetic variation via mutation-selection balance: Lerch's Zeta meets the abdominal bristle. Theor. Popul. Biol. 25: 138-193. Quantitative Genetics: finding the genes 1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors controlling bristle score in Drosophila melanogaster II Distribution of third chromosome bristle effects within chromosome sections. Genetics 118: 445-459. 2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of complex traits: from polygenic to omnigenic. Cell 169: 1177-1186. Neutral Evolution 1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science. 217:624-626. 2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural Selection. Molecular Biology and Evolution 110: 21077-6. 2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M., Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112: 2109-4. 2c. Ellegren & Galtier. 2016. Determinants of genetic diversity. Nature Reviews Genetics. Mutation and Genetic Variability 1. Luria, S. E., and M. Delbrück. 1943. Mutations of Bacteria from Virus Sensitivity to Virus Resistance. Genetics. 28(6):491-511. 2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.) 79: 142-45. Testing for selection 1. McDonald & Kreitman. 1991. Adaptive protein evolution at the Adh locus in Drosophila. Nature. 2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999). 3. Siddiq et al. 2016. Experimental test and refutation of a classic case of molecular adaptation in Drosophila melanogaster. Nature Ecology & Evolution. The shifting balance 1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the VI International Congress of Genetics: 1. pp 356-366. 2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's shifting balance theory of evolution. Evolution 51: 643-671. 3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and the "Shifting Balance". Genetics. Evolution of Sex 1. Muller, H.J. 1964. The relation of recombination to mutational advance. Mutation Res. 1(1):2-9 2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of molecular evolution. Nature. Kin Selection, Cooperation, and Conflict 1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I. Journal of Theoretical Biology. 7:1-52. 2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist. 14(1):249-264. Sexual Selection 1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor. Biol. 53:205-214. 2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating preferences and the paradox of the lek. Nature. 350:33-38. Fitness Landscapes 1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment Interactions. Genetics. 139:19-33. 2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science. Speciation 1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila. Evolution. 43:362-381. 2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread within species. Nature. 2. *Marcos Antezana:* Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department of Biology. 3. *Remco Folkertsma:* 1. The work by Hopi Hoekstra on local adaptation and oldfield mice 2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller, I., ... & Wolf, J. B. (2014). The genomic landscape underlying phenotypic integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414. 4. *Joshka Kaufmann and Leslie Turner* They offer us a link to 'papers every evolutionary biologist should read', the papers are collected by Leslie Turner. https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf 5. *Sarah Stockwell* Matt Ridley collected classic papers in evolutionary biology and printed part of these papers in his book Evolution (see Matt Ridley. Evolution (Univ. of Oxford Press, 2nd edition, 2004))]]> Shruti Paniwala https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics Thu, 04 Sep 2014 17:46:41 -0500 https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics <![CDATA[Which math/statistics programming language/application do you most frequently use in bioinformatics?]]> I'm doing a bit more statistical analysis on some bioinformatics things lately, and I'm curious if there are any programming languages that are particularly good for this NGS computation. What suggestions do you guys have? Are there any languages that have exceptionally good libraries?

]]> John Parker https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics Fri, 05 Jan 2018 04:04:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35059/lrcstats-long-read-correction-statistics <![CDATA[LRCstats: Long Read Correction Statistics]]> LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats

]]> Jit https://bioinformaticsonline.com/researchlabs/view/35868/simpson-lab Tue, 06 Mar 2018 08:59:09 -0600 <![CDATA[Simpson Lab]]> We are the Statistical Bioinformatics group in the Institute for Adaptive and Neural Computation in the School of Informatics at the University of Edinburgh. The group is led by Dr. Ian Simpson who is a Lecturer in Biological Informatics in the School of Informatics at Edinburgh University. Details to follow....

http://statbio.github.io

]]> https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization Thu, 04 Apr 2024 01:44:50 -0500 https://bioinformaticsonline.com/bookmarks/view/44508/a-web-based-tool-for-sequence-alignment-statistics-and-innovative-visualization <![CDATA[A web-based tool for sequence alignment statistics and innovative visualization]]> AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.

Address of the bookmark: https://bioinformatics.um6p.ma/AlignStatPlot/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/27094/smash-an-alignment-free-method-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences Tue, 26 Apr 2016 12:18:49 -0500 https://bioinformaticsonline.com/bookmarks/view/27094/smash-an-alignment-free-method-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences <![CDATA[Smash: An alignment-free method to find and visualise rearrangements between pairs of DNA sequences]]> Smash is a completely alignment-free method/tool to find and visualise genomic rearrangements. The detection is based on conditional exclusive compression, namely using a FCM (Markov model), of high context order (typically 20). For visualisation, Smash outputs a SVG image, with an ideogramoutput architecture, where the patterns are represented with several HSV values (only value varies). The method can perform both in small- and large-scale. Nevertheless is more directed to large-scale since that the main aim of the research is to know where the large-scale [chromosomal by chromosome] of several primates was equal/different, having at a glance a map of the entire genomes.

Address of the bookmark: http://bioinformatics.ua.pt/software/smash/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40946/free-genomics-data Fri, 07 Feb 2020 14:08:31 -0600 https://bioinformaticsonline.com/bookmarks/view/40946/free-genomics-data <![CDATA[Free Genomics data !]]> The specimens were collected by the Oxford Wytham Woods and Edinburgh Lohse lab teams. DNA extraction and sequencing was carried out by the Sanger Institute Scientific Operations teams. Assemblies were carried out by the Tree of Life team (Shane McCarthy) and colleagues in Pacific Biosciences (Jonas Korlach).

https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Address of the bookmark: https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/21703/coding-ground Tue, 17 Mar 2015 00:47:20 -0500 https://bioinformaticsonline.com/bookmarks/view/21703/coding-ground <![CDATA[Coding Ground]]> Online coding group for most of the programming languages.

Code in almost all popular languages using Coding Ground. Edit, compile, execute and share your projects, 100% cloud.

http://www.tutorialspoint.com/codingground.htm

Address of the bookmark: http://www.tutorialspoint.com/codingground.htm

]]> Jitendra Narayan