Download BIPASS: Bioinformatics pipelines alternative splicing services

BIPASS: Bioinformatics pipelines alternative splicing services
Mentor: Dr. Zoé Lacroix
Location: Scientific Data Management Lab. Room GWC 443
The proposed work will contribute to scientific discovery in support of comparative genomics and
evolution of proteins; the understanding of the mechanisms underlying genome variation in general
and splice site variation in particular will allow the identification of new targets for studying
biological systems in humans, model organisms, and microbial environments.
In 2004, Lacroix's laboratory at ASU has developed BIPASS a resource that supports alternative
splicing events analysis [LLR+07]. BIPASS provides access to transcript sequences retrieved from
GenBank that have been pre-processed, clustered with respect to the gene from which they are
transcribed, and stored in a database. BIPASS also offers the ability to cluster on-line transcript
sequences submitted by scientists. The main findings of the project included (1) the ad-hoc
workflow to populate the database did not support updates: the whole database had to be recomputed from scratch each time, (2) this also prevented the ability to combine transcripts
submitted by scientists into the existing clusters, (3) the need to use a reference genome without
any record of its provenance, the inability to offer real operators to users to query their transcripts
against the database. More recent work led to the design of a prediction method to identify
alternative splicing events that may lead to mature mRNA that have not yet been observed. This
method exploits a partial order to generate a splicing maturity tree from each transcript cluster, as
illustrated in Figure 5 [LL08]. Our aim is to exploit the proposed methodology to generate
explanations of the consequences of splice-site mutations.
Proposed Method for Splice Form Assessment: We will first develop a method for gene
discovery and then a technique to support the identification of mutations that may affect splicing,
thus protein products. Specifically, given an input sequence (DNA or RNA) and the location of a
mutation, we will be able to exploit transcription factors as specific short sequence encoded on
specific dimensions and information known about splicing events to (1) identify the signals
controlling an encoded gene (here 'signal' refers to a transcription factor as illustrated in Figure 6)
and (2) identify the proteins the mutated gene is likely to produce. Moreover, (3) we will infer the
properties of these proteins from protein domain classifications. This will exploit both new
methodologies developed in our lab. as well as information that is publicly available on the web
such as for genomes (UCSC, http://genome.ucsc.edu/), sequences (GenBank, NCBI,
http://www.ncbi. nlm.nih.gov/Genbank/), proteins (Swiss-Prot http://www.expasy.ch/sprot/), and
known protein classifications (such as SCOP, http://scop.mrc-lmb.cam.ac.uk/scop and Pfam
http://pfam. sanger.ac.uk). We will use known promoters including TATA box, cis-trans regulator
elements, Ribosomal Binding Site (RBS), enhancers and repressor region, CCAAT-box, GC-box,
Downstream Promoter Elements (DPE), Transcription Start Site (TSS+1 site equivalent),
Transcription Factor Binding sites (TFBS), B recognition element (BRE), insulators, and initiators
[Dat]. These promoters combined with start (ATG) and stop (TAA, TGA, TAG) codons and splicing
sites (ESE, ISE, ESS, ISS, and SSM) will constitute the blocks of our method. Indeed we will
describe the potential coding regions as words generated by languages such as illustrated by the
simplified language AB(CD)+E where A is a transcription site, B is an initiator, C is a translation site,
D is a splicing site, and E is a stop codon. The method will be used to predict genes on DNA
sequences, and a subsequent alignment with the genome will identify the genes the input
sequence originates from. We propose to implement the method with the multidimensional feature
extraction algorithms. Unlike traditional alignment methods, they allow the encoding of multiple
patterns and the identification of successive patterns in the input sequence. These criteria are
critical to the success of the proposed activity.
Figure 5. Splicing maturity tree.
Figure 6. Language for gene prediction (Eukaryotes only).
Proposed Evaluation We will evaluate our proposed methods and algorithms. Gene prediction
methods can be evaluated through gene information already published and available. Moreover,
we will compare our algorithm against existing tools (benchmark). We will use known splice site
mutation events from OMIM to evaluate our splice-site mutation evaluation tool by simulating the
effects of splice-site and splice-control site mutations for known cases. We will use simulated
datasets to evaluate the prediction methods. However, we will verify manually the results with
respect to known published data. We will present and submit papers for peer-review.
[LLR+07] Z. Lacroix, C. Legendre, L. Raschid, and B. Snyder, “BIPASS: Bioinformatics
pipelines alternative splicing services,” Nucleic Acids Research, vol. 35, web server
issue, Suppl. 2, pp. 292-296, July 2007.
[LL08]
Z. Lacroix and C. Legendre, “BIPASS: Design of alternative splicing services,”
International Journal Computational Biology and Drug Design, vol. 1, no. 2, pp. 200–
217, 2008.
To apply, a student must contact Dr. Lacroix at [email protected] with a resume upto-date and a letter of motivation (stipulating the reasons why you are interested by the
project, if you have prior knowledge of the problems addressed, if you are interested to
participate in a cross-disciplinary project, etc.)