<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/35384?offset=390 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/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context Fri, 14 Jul 2017 07:47:03 -0500 https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context <![CDATA[GView: A Java application for viewing and examining prokaryotic genomes in a circular or linear context]]> GView is a Java application for viewing and examining prokaryotic genomes in a circular or linear context. It accepts standard sequence file formats and an optional style specification file to generate customizable, publication quality genome maps in bitmap and scalable vector graphics formats. GView features an interactive pan-and-zoom interface, a command-line interface for incorporation in genome analysis pipelines, and a public Application Programming Interface for incorporation in other Java applications.

Availability: GView is a freely available application licensed under the GNU Public License. The application, source code, documentation, file specifications, tutorials and image galleries are available at http://gview.ca

Contact: ac.cg.cpsa-cahp@raalesmod.nav.yrag

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/42987/public-databases-for-bioinformatics Tue, 23 Mar 2021 05:32:15 -0500 https://bioinformaticsonline.com/bookmarks/view/42987/public-databases-for-bioinformatics <![CDATA[Public Databases for Bioinformatics !]]> https://www.nature.com/articles/s41467-020-17155-y

Server Infrastructure: File Server: dhara: Synology 3614 Storage Appliance 4 Core Xeon 108TB disk storage 10Gb ethernet to SCG3 Access atx: dhara:5000 Has btsync server (try it - its much better than dropbox) Compute Servers: nandi: Kundaje and Phi Server 24 intel cores 256GB RAM 500GB of SSD storage 36TB RAID6 local storage 4 Intel Phi's (space for 4 more GPU's) durga: Montgomery and sensitive data 24 intel cores 256GB RAM 500GB of SSD RAID0 storage 60TB RAID6 local storage mitra: Bassik and Web/DB Server 24 core 256GB RAM 500GB of SSD RAID0 storage 36TB RAID6 local storage vayu: Kundaje GPU server 4 core 64GB RAM 200GB of SSD storage 8TB RAID10 local storage 4 Nvidia GTX 970 4GB GPUs amold: Bickel and SGE server 32 AMD core 128GB RAM 200GB of SSD storage 12TB RAID5 local storage wotan: Bickel and SGE server 64 AMD core 256GB RAM 200GB of SSD storage 12TB RAID5 local storage Filesystem: /users/$USER default home directory full backups nightly nfs mount to dhara should store code, papers, and other highly processed data here /mnt/data/ globally accessible data should store common data here e.g. genomes and indexes, annotations, ENCODE data if you dont want this to count towards your quote you must chown /mnt/lab_data/$LAB/ lab accessible data should store lab project data here e.g. ATAC-seq prediction data, enhancer prediction, motif calls /srv/scratch/$USER fast local storage not backed up, but on raid and data will never be deleted most analysis should be performed here /srv/persistent/$USER fast local storage synced nightly, but not backed up ie if the hard drives fail or you delete something and notice within 24 hours we can recover. Otherwise not. (vs home which is properly backed up ) intermediate analysis products that would be hard to recover should be stored here e.g. stochastic analysis results that need to be kept so that paper results can be reproduced /srv/www/$LABNAME/ web accessible from mitra.stanford.edu *NOT BACKED UP* Some parallel programming patterns: # gzip a bunch of files parallel gzip -- *.FILESTOGZIP # fork example in python: (for more detailed examples look at https://github.com/nboley/grit/ grit/lib/multiprocessing_utils.py) import os import time import random import multiprocessing class ProcessSafeOPStream( object ): def __init__( self, writeable_obj ): self.writeable_obj = writeable_obj self.lock = multiprocessing.Lock() self.name = self.writeable_obj.name return def write( self, data ): self.lock.acquire() self.writeable_obj.write( data ) self.writeable_obj.flush() self.lock.release() return def close( self ): self.writeable_obj.close() def worker(queue, ofp): # Try without this random.seed() while True: i = queue.get() if i == 'FINISHED': return # simulate an expensive function x = random.random() time.sleep(x/10) print i, x ofp.write("%i\t%s\n" % (i, x)) NSIMS = 10000 NPROC = 25 # populate queue todo = multiprocessing.Queue() for i in xrange(NSIMS): todo.put(i) for i in xrange(NPROC): todo.put('FINISHED') ofp = ProcessSafeOPStream( open("output.txt", "w") ) pids = [] for i in xrange(NPROC): pid = os.fork() if pid == 0: worker(todo, ofp) os._exit(0) else: pids.append(pid) for pid in pids: os.waitpid(pid, 0) ofp.close() print "FINISHED"

For use case 1 we obtained the following ENCODE and ROADMAP datasets https://www.encodeproject.org/files/ENCFF446WOD/@@download/ENCFF446WOD.bed.gz, https://www.encodeproject.org/files/ENCFF546PJU/@@download/ENCFF546PJU.bam, https://www.encodeproject.org/files/ENCFF059BEU/@@download/ENCFF059BEU.bam. Blacklisted regions were obtained from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz. The human genome version hg38 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz.

For use case 2 we used the set of narrowPeak files summarized in https://github.com/wkopp/janggu_usecases/tree/master/extra/urls.txt (archived version v1.0.1). The human genome version hg19 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz

For use case 3 we used the ENCODE datasets https://www.encodeproject.org/files/ENCFF591XCX/@@download/ENCFF591XCX.bam, https://www.encodeproject.org/files/ENCFF736LHE/@@download/ENCFF736LHE.bigWig, https://www.encodeproject.org/files/ENCFF177HHM/@@download/ENCFF177HHM.bam as we as the GENCODE annotation v29 from ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz.

Address of the bookmark: http://mitra.stanford.edu/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation Wed, 29 Nov 2017 07:40:22 -0600 https://bioinformaticsonline.com/bookmarks/view/34482/ribbon-visualizing-complex-genome-alignments-and-structural-variation <![CDATA[Ribbon: Visualizing complex genome alignments and structural variation:]]> Ribbon can be used for long reads, short reads, paired-end reads, and assembly/genome alignments. Instructions for each data format are available by clicking on "instructions" in each tab on the right.

Local installation:

You can install Ribbon locally from Github by following the instructions here: https://github.com/MariaNattestad/Ribbon

Address of the bookmark: http://genomeribbon.com/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool Fri, 08 Dec 2017 17:41:14 -0600 https://bioinformaticsonline.com/bookmarks/view/34571/mugsy-multiple-whole-genome-alignment-tool <![CDATA[Mugsy: multiple whole genome alignment tool]]> Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.

To cite Mugsy, use:

Angiuoli SV and Salzberg SL. Mugsy: Fast multiple alignment of closely related whole genomes.Bioinformatics 2011 27(3):334-4

Address of the bookmark: http://mugsy.sourceforge.net/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome Tue, 26 Dec 2017 22:23:39 -0600 https://bioinformaticsonline.com/bookmarks/view/34867/magic-blast-a-tool-for-mapping-large-next-generation-rna-or-dna-sequencing-runs-against-a-whole-genome-or-transcriptome <![CDATA[Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.]]> Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41158/carefully-opt-for-human-reference-genome Tue, 18 Feb 2020 07:43:32 -0600 https://bioinformaticsonline.com/bookmarks/view/41158/carefully-opt-for-human-reference-genome <![CDATA[Carefully opt for human reference genome]]> Heng Li posted several issues with the human reference genomes given in these resources and suggests the following compressed FASTA file to be used as hg38/GRCh38 human reference genome.

if you map reads to GRCh38 or hg38, use the following:

ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz

There are several other versions of GRCh37/GRCh38. What’s wrong with them? Here are a collection of potential issues:

More at http://lh3.github.io/2017/11/13/which-human-reference-genome-to-use

Address of the bookmark: http://lh3.github.io/2017/11/13/which-human-reference-genome-to-use

]]> biogeek https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads Fri, 11 May 2018 05:07:45 -0500 https://bioinformaticsonline.com/bookmarks/view/36533/mecat-fast-mapping-error-correction-and-de-novo-assembly-for-single-molecule-sequencing-reads <![CDATA[MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads]]> MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current PBcR-Mhap pipeline. MECAT performance were compared with PBcR-Mhap pipeline, FALCON and Canu(v1.3) in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the PBcR-Mhap pipeline and FALCON. 

https://www.nature.com/articles/nmeth.4432

Address of the bookmark: https://github.com/xiaochuanle/MECAT

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/36918/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads Tue, 12 Jun 2018 08:14:41 -0500 https://bioinformaticsonline.com/bookmarks/view/36918/p-rna-scaffolder-a-fast-and-accurate-genome-scaffolder-using-paired-end-rna-sequencing-reads <![CDATA[P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads]]> Address of the bookmark: http://www.fishbrowser.org/software/P_RNA_scaffolder/

]]> Jit https://bioinformaticsonline.com/file/view/36952/getoptspl-file Fri, 15 Jun 2018 04:43:03 -0500 https://bioinformaticsonline.com/file/view/36952/getoptspl-file <![CDATA[getopts.pl file]]> SSPACE_longread complain for getopts.pl file.

To resolve this, download and have in SSPACED-Longreads folder.

Cheers :)

]]> Jit