Bioinformatic Methods I - https://www.coursera.org/learn/bioinformatics-methods-1/
https://blast.ncbi.nlm.nih.gov/Blast.cgi
Program Query Database Alignment # Searches Uses blastn DNA DNA DNA 1 find homologous DNA sequences tblastx DNA DNA protein 36 find homologous proteins from unannotated query and db sequences blastx DNA protein protein 6 identify proteins in query DNA sequence tblastn protein DNA protein 6 find homologous proteins in unannotated DNA DB blastp protein protein protein 1 find homologous proteins
Mega X https://www.megasoftware.net/
Global alignments are those in which the set of sequences to be aligned are similar across their entire length Local alignments are those in which there is similarity only at particular sub-regions of the sequence.
Clustal DIALIGN - http://dialign.gobics.de/ MAFFT - http://mafft.cbrc.jp/alignment/server/
MEGA X http://www.megasoftware.net/ PHYLIP http://evolution.genetics.washington.edu/phylip.html Web-based ML analysis http://bar.utoronto.ca/webphylip/
dN/dS Ratio Test It is often useful to check your aligned DNA sequences using the TRANSEQ app of EMBOSS suite of tools from the EBI. Go to http://bar.utoronto.ca/EMBOSS Datamonkey (http://www.datamonkey.org/) to look for selection in our sequences
RNA-Seq Analysis
Plant MPSS databases homepage, at http://mpss.danforthcenter.org/ 1
METAGENOMICS
Bioinformatic Methods II - https://www.coursera.org/learn/bioinformatics-methods-2/
CDD – Conserved Domain Database http://www.ncbi.nlm.nih.gov/
SMART http://smart.embl-heidelberg.de/
InterproScan http://www.ebi.ac.uk/interpro/search/sequence-search
List PPI databases: http://ppi.fli-leibniz.de/jcb_ppi_databases.html
DIP http://dip.doe-mbi.ucla.edu/dip/Main.cgi
BIOGRID http://www.thebiogrid.org/index.php
Cytoscape http://www.cytoscape.org/download.html
Bioinformatic Methods I - https://www.coursera.org/learn/bioinformatics-methods-2/
know why we are interested in searching for motifs and profiles in sequences; know the advantages and disadvantages of representing structural elements in protein sequences as motifs or profiles; be able to generate a motif given an alignment; understand how to score a given sequence with a PSSM or profile HMM; be able to use CDD, CDART, SMART, Pfam, and InterProScan to identify specific functional units within protein sequences;
CDD – Conserved Domain Database http://www.ncbi.nlm.nih.gov/
CDART
SMART http://smart.embl-heidelberg.de/
InterProScan http://www.ebi.ac.uk/interpro/search/sequence-search
understand why protein-protein interactions are important biologically, and also how they may be determined experimentally; be able to assess the advantages and disadvantages of the methods for determining protein-protein interactions; know the terminology associated with protein-protein interaction graphs; be able to use DIP, BioGRID and Cytoscape to identify interacting proteins for your gene product of interest and to filter and decorate networks based on additional information; be able to identify the type of support for a given interaction in a given database; be able to interpret the other types of information (GO categories) provided by the software tools.
DIP http://dip.doe-mbi.ucla.edu/dip/Main.cgi
BioGRID http://www.thebiogrid.org/index.php
Cytoscape
know the main methods for determining protein structure; be familiar with Protein Database records and how to determine which method was used to ascertain a given protein’s structure; be able to view the protein’s structure both with the Jsmol applet and PyMOL; be able to use PyMOL to view the structure model in different representations (ribbon, stick etc.) and colour different parts of the molecule at will; be able to align two structures using the command line interface of PyMOL; be able to highlight certain residues in the Sequence Viewer part of PyMOL and have these displayed in the Structure Viewer window be able to measure inter-atom distances using the Measure tool of PyMOL.
Protein DataBank https://www.rcsb.org/
NCBI Vast http://www.ncbi.nlm.nih.gov/Structure/VAST/vastsearch.html
PyMOL
know the main technologies for creating expression data and for extracting information from microarray hybridizations or RNA-seq reads; understand the sources of error associated with the outputs of a transcriptomics experiment; understand the importance of normalization and how to interpret MA and box plots; be able to select significant genes and to organize them using hierarchical clustering – what does the Pearson correlation coefficient score measure?; know how to use gene expression databases to leverage preexisting expression data; be able to use BioConductor for normalization, selecting significantly differentially expressed genes, creating heatmaps, and clustering; be able to use some online tools to identify coexpressed genes using existing publiclyavailable gene expression data and to view their expression patterns; be familiar with gene expression databases.
Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA) http://www.ncbi.nlm.nih.gov/sra
BioConductor http://bar.utoronto.ca/BioC/
Expression Angler http://bar.utoronto.ca/ntools/cgi-bin/ntools_expression_angler.cgi
Expression Browser http://bar.utoronto.ca/affydb/cgi-bin/affy_db_exprss_browser_in.cgi
eFP Browser http://bar.utoronto.ca/efp/
ATTED-II http://atted.jp/
GeneMANIA http://genemania.org/
know the main technologies for identifying transcription factor binding sites in vitro; be able to name some methods for identifying potential transcription factor binding sites in silico; understand how word-count and Gibbs sampling methods work and the differences between these two methods; understand why we would want to generate results which referenced a background set of all promoters; know how to use online tools, such as TF2Network or JASPAR to identify known ciselements in promoters of coexpressed genes; be able to use online versions of MEME.
Promomer http://bar.utoronto.ca/
TF2Network http://bioinformatics.psb.ugent.be/webtools/TF2Network/
JASPAR and Cistome http://bar.utoronto.ca/cistome_legacy/cgi-bin/BAR_Cistome.cgi http://jaspar.genereg.net/
MEME http://meme-suite.org/tools/meme
Human eFP Browser http://bar.utoronto.ca/efp_human/cgi-bin/efpWeb.cgi
Plant Bioinformatics - https://www.coursera.org/learn/bioinformatics-methods-1/
Araport Arabidopsis thaliana only Araport11 release purveyor; general information database served through web page. TAIR Arabidopsis thaliana only TAIR10 release purveyor; general information database served through web page. Gene Ontology curator. Gramene Many plants Presents information from many databases through a simpler web application-like design. Ensembl Plants Many plants Presents information from many databases through web pages with embedded web applications. Easy to access search tools. SUBA4 Arabidopsis thaliana only Presents consensus subcellular localization data from a variety of sources. Cell eFP Browser Arabidopsis thaliana only Visual representation of subcellular localization data through simple tool. PLAZA Many plants Presents information from many databases. Easy to access tools. Custom tree creation. 1001 Genomes Arabidopsis thaliana only Database of mutations across >1001 Arabidopsis accessions (strains).