Workflow for promoter identification using Microarray data

Summary:

The workflow helps us to identify consensus patterns in the upstream regions of certain genes, which may be clustered together, which may also lead to the identification of novel target genes for therapeutic purposes. Gene expression data is given as input for the workflow, for which the data is supposed to be pre-normalized at the user’s end. The normalized data is then parsed for Cluster analysis (preferably K-Means or hierarchical Clustering). For the different clusters obtained, the corresponding gene ids are obtained from database followed by retrieval of the upstream regions of the gene. Motif analysis tool MEME is used to identify conserved patterns/motifs. The motifs thus obtained are searched against the desired motif databases using the tool MAST.

Input files required for this workflow:

1. Gene expression data in .pcl format with the first column or UID column having the standard NCBI accession id, or locus tag/synonym or PID (protein ID).
2. The desired database to be searched (in fasta format) with the reported motifs by MAST tool.

Fig:Translated implementation

Standard Tools:

CLUSTER:

Perform a variety of types of cluster analysis and other types of processing on large microarray datasets. Currently includes hierarchical clustering, k-means clustering, etc.

MEME:

MEME is a tool for discovering motifs in a group of related DNA or protein sequences.

MAST:

MAST is a tool for searching biological sequence databases for sequences that contain one or more of a group of known motifs.

Custom Tools:

• iSRT (intergenic Sequence Retrieval Tool)

It is a custom tool that retrieves the upstream (intergenic) regions of genes, given the gene ids, from the database of intergenic regions. It takes as input a list of gene ids (synonym OR protein_id OR PID), minimum length of the retrieved sequences, the sequence start site and the sequence end site as arguments and retrieves the desired sequences from the intergenic database.

Usage of this tool: ./intSeqRetreiver.pl id_file_name(inputFile) column_name minimum_req_length required_length_of_db_sequence startPosition endPosition output_file_name

Where,

- intSeqRetreiver.pl is the tool

- d_file_name is the file which contains the list of synonyms OR protein_ids OR PIDs for which the sequence is to be retrieved in FASTA format (with complete path);

- column_name for unique identification of an organism (whether synonym OR protein_id OR PID)

- minimum_req_length is the minimum length of retrieved sequences (for eg, a sequence containing atleast 50 nucleotides should be considered)

- required_length_of_db_sequence is the maximum length of the sequence required

- Seqstart is the start position from where the sequence is to be retrieved in the database

- Outfile is the name of the output file in which the output is to be written.

Output of this tool:

1.      A multi-fasta formatted text file containing the intergenic sequences

Parsers:

• Cluster to MEME Parser

This parser gives a textual output of the list of gene ids according to the clusters formed. According to the co-relation cut-offs mentioned, the parser clubs the set of genes which fall in the same cluster. According to the gene of interest provided by the user, the cluster containing the gene of interest is passed on to iSRT (intergenic Sequence Retrieval Tool) and upstream sequences of the gene ids are retrieved on which MEME is run.