Services and software

The bioMoby project aims to provide bioinformatics resources through the web. In this regard, we have designed and implemented a set of Web services that are compliant with bioMoby specifications. We have been focusing on providing genome analysis resources. These resources are of various types:

• Sequence analysis
  • Gene prediction (i.e. runGeneIDGFF, runSGP2GFF)
  • Signal predicition (transcription regulatory elements or splicing elements) [i.e. runMatScanGFF, runMetaAlignmentGFF]
  • ESTs assembly
• Sequence data retrieval (promoter sequences from Ensembl database)
• Data format conversion

A more complete list of Web resources can be found in the WEB SERVICES section We have also set up some pipelines of analysis to illustrate the use of these Web resources. Here are the main pipelines of computational analysis that were implemented:

• Promoter analysis
• ESTs assembly

More information about our pipelines of analysis can be found in the WORKFLOWS section.

geneid is a program to predict genes along a DNA sequence in a large set of organisms. While its accuracy compares favorably to that of other existing tools, geneid is more efficient in terms of speed and memory usage and it offers some rudimentary support to integrate predictions from multiple source.

• geneid homepage
• geneid server

You will also find whole genome annotations for different species obtained with geneid in our "Gene Predictions" web pages.

Obtaining plots to compare genomic sequences and/or sources from GFF files. Last available version is 0.98. Get the PostScript version of "gff2ps Users Manual" (v0.96). A Web Server is also available at Institut Pasteur thanks to Catherine Letondal. A new section has been created: HTML HOWTOs for gff2ps. The first two HOWTOs were also added: "Comparing sources with gff2ps" and "Visualizing PostScript output from gff2ps". We hope you will find them useful.

gff2ps was used to obtain the six chromosome arm plots (X, 2L, 2R, 3L, 3R and 4) appearing in the "Coding content of the fly genome" genome map (figure 4), included as a poster in "The Genome Sequence of Drosophila melanogaster" [Adams et al. Science 287(5461):2185-2195(2000)].

We have produced the map of the Human Genome with gff2ps. 22 autosomic, X and Y chromosomes were displayed in a big poster appearing as the figure 1 of "The Sequence of the Human Genome" [Venter et al. Science 291(5507):1304-1351 (2001)]. The single chromosome pictures can be accessed from here to visualize the web version of the "Annotation of the Celera Human Genome Assembly" poster.

gff2ps has achieved another genome landmark. The mosquito genome annotation for five chromosome arms (2L, 2R, 3L, 3R and X) has been summarized into a two-sided five-pages foldout included as the figure 1 of "The Genome Sequence of the Malaria Mosquito Anopheles gambiae" [Holt et al. Science 298(5591):129-149 (2002)], available from the "Annotation of the Anopheles gambiae genome sequence" web page.

meta is a program to produce and to align the TF-maps of two gene promoter regions. meta is very useful to characterize promoter regions from orthologous genes, or from co-regulated genes in microarrays, as it reduces the signal/noise ratio in a very significant manner, still detecting the real functional sites.

• meta homepage
• meta server

overlap is a program that computes the overlap between two sets of genomic features. More precisely it takes two gff files of genomic features as input and for each feature of the first set, says whether it is overlapped by a feature of the second set (basic mode, however more and more precise information can be retrieved).

EXECUTE INSTRUCTIONS:

After getting the overlap executable, type:

./overlap

without any argument to get the help.

This will give you all the possible options of overlap.  Basically the output will be equal to the first input file (file1) with additional information about the overlap of its features with the features of the second file (file2).  There are 4 basic modes:- Mode 0 (option -m 0) is to report boolean overlap: 1 if the feature of file1 is overlapped by a feature of file2, 0 otherwise.- Mode 1 (option -m 1) is to report quantitative overlap: the number of file2 features overlapping a file1 feature. - Negative mode (option -m -1 for example) is to report the list of coordinates of file2 features overlapping a file1 feature.- Value mode (option -m n where n>=10 and n is even) is to report the list of values located in field 10 in file2 associated to file2 features overlapping a file1 feature.

You can also ask for inclusion instead of general overlap, this is the option -i, or make a stranded overlap, this is the option -st.

Do not hesitate to contact sarahqd at gmail dot com for more information.

• overlap (64 bit linux)
• overlap (32 bit linux)

project is a program that projects genomic features onto their sequences. Please contact Sarah Djebali (sarah dot djebali at crg dot es for any question).

• project (64 bit linux)
• project (32 bit linux)

Selenoproteins are a group of proteins that contain selenocysteine (Sec), a rare amino acid inserted co-translationally into the protein chain. The Sec codon is UGA, which is normally a stop codon. In selenoproteins UGA is recoded to Sec in presence of specific signals on selenoprotein gene transcripts. Due to the dual role of the UGA codon, gene prediction programs fail to predict correctly selenoproteins. Selenoprofiles is an homology-based in silico tool able to scan genomes for members of the known selenoprotein families, thus finding both selenoproteins and cysteine homologues. Selenoprofiles is built in python, and it internally runs psitblastn, exonerate, genewise and SECISearch.

Selenoprofiles is tuned to search for selenoprotein genes, and comes out-of-the box with profile alignments for each known selenoprotein and selenocysteine-related family (Note: profiles will be released soon. The current release contain only the program and a single profile for example).

Selenoprofiles can be used to search for any protein family (also non-selenoprotein), given an input profile alignment. This pipeline combines standard gene prediction tools to provide a clean and fast way to scan genomes for protein families, and provides a wide repertoire of output formats which can also be extended by the user. The program allows for a deep level of customization, and provides many built-in methods to filter spurious hits.

Selenoprofiles version 2.2 is now available. Version 1 is no longer maintained -- if you need help with version 1, contact us by mail. This version features major improvements on the previous ones, such as:

• improved workflow control
• prediction by blast can be output, allowing use of selenoprofiles in bacterial genomes (exonerate and genewise are eukaryote specific)
• lazy computing implemented
• pre-clustering of the profile alignment: multiple blast are run if the profile is highly variable
• an SQLite database is used to store results, allowing to search for a high number of families without producing an enourmous amount of files, since they can be deleted at the end of computation
• improved customization of the options used with the slave programs, which can potentially be different for each profile
• improved filtering of results: all filtering procedures are defined as pieces of python code which are run internally in selenoprofiles. Several methods useful for filtering are provided. Filtering can be customized for each family
• intra-family and inter-family redundancy of results is removed
• tag blast and gene ontology extensions implemented for filtering (see manual)

Tools for graphical representation of selenoprofiles results are under development and will be released in the next few months.

MANUAL

Download the last version of selenoprofiles manual here: http://genome.crg.es/~mmariotti/selenoprofiles_manual.pdf 

INSTALLATION:

For selenoprofiles to work, all the slave programs that it utilizes must be  already installed in your machine (blastall, exonerate, genewise). You will also need some external python modules if you want to use all its functionalities. These additional modules are needed if you want to scan genomes for selenoproteins, but may be omitted if you want to scan for your protein family of interest. In this page you can find help to install the slave programs and the additional python modules.

You can either use svn to fetch the latest selenoprofiles package:

  svn co --username guest --password guest svn://svn.crg.es/big/selenoprofiles/trunk selenoprofiles

or download this tarball. Then, follow the instructions in the README.

CITATION:

Selenoprofiles was published in Bioinformatics. To read the article or access the online data, check this page. Please cite:

Mariotti M, Guigo R - Selenoprofiles: profile-based scanning of eukaryotic genome sequences for selenoprotein genes.Bioinformatics. 2010 Nov 1;26(21):2656-63. Epub 2010 Sep 21

Contact: marco.mariotti@crg.eu

SymCurv is a computational ab initio method for nucleosome positioning prediction. It is based on the structural property of natural nucleosome forming sequences, to be symmetrically curved around a local minimum of curvature. The method takes as input the primary DNA sequence, calculates the expected curvature from which it deduces possible centers of nucleosomal sequences, by imposing symmetry constraints. SymCurv's performance is comparable to existing tools but offers the additional advantages of predicting nucleosome positions under two assumed-states (stationary and dynamic) providing insight on the remodelling potential of nucleosomes of possible regulatory function.

• SymCurv WebServer
• SymCurv Documentation