Gene ID conversion resources

I see lot of my friends spending time on researching upon databases. There is a dire requirement for compiling all the available databases in a useful and user friendly way. 

Here, let's see the currently available Gene ID conversion resources:

NetAffx: http://www.affymetrix.com/analysis/index.affx

It has batch and net affx query, provides detailed description of individual probeset and allows user to group probsets according to annotation type. Covers 10 species and has 17 possible conversions. 

g:Profiler: http://biit.cs.ut.ee/gprofiler/gconvert.cgi

Input can be a mix of different ID types. User has to choose only the desired output. It covers 31 species and has 14 possible conversions.

Babelomics ID Converter: http://babelomics3.bioinfo.cipf.es/  

GeneID alone as input ID type. Covers 11 species and 36 output ID types are available.

BioDBnet:http://biodbnet.abcc.ncifcrf.gov/db/db2db.php

The most comprehensive and very easy to use database wherein we can give any ID as input and get the output in any convertible ID format.

DAVID: http://david.abcc.ncifcrf.gov/conversion.jsp

User can upload more than one file of different ID types. A specific format is not available and it has 32 possible conversion.

Clone/Gene ID converter:http://idconverter.bioinfo.cnio.es/

Gene ontology, pathway and literature references are available. Provides chromosome location for human. Covers 3 species and has 25 possible conversions.    

The Synergizer: http://llama.mshri.on.ca/synergizer/translate/

It is a repository of gene and protein identifier synonym relationships. The number of species covered changes based on the 'authority' used and accordingly the number of input and output format also change.

 

MUSIC

We all listen to music. It may be RAP, Rock, Hard metal or melodious any kind of music form may remain close to heart.
But, here is the Bioinformatics music. MUSIC, a signal processing approach for identification of enriched regions in ChIP-Seq data, available at music.gersteinlab.org.
MUSIC first filters the ChIP-Seq read-depth signal for systematic noise from non-uniform mappability, which fragments enriched regions. Then it performs a multiscale decomposition, using median filtering, identifying enriched regions at multiple length scales.
This is useful given the wide range of scales probed in ChIP-Seq assays. MUSIC performs favorably in terms of accuracy and reproducibility compared with other methods.
So, start listening Oops working upon your MUSIC.

OncoCis

Whole genome sequencing has enabled the identification of thousands of somatic mutations within non-coding genomic regions of individual cancer samples. However, identification of mutations that potentially alter gene regulation remains a major challenge. OncoCis is a new method that enables identification of potential cis-regulatory mutations using cell-type specific genome and epigenome-wide datasets along with matching gene expression data. OncoCis demonstrates that the use of cell-type specific information and gene expression can significantly reduce the number of candidate cis-regulatory mutations compared with existing tools designed for the annotation of cis-regulatory SNPs. The OncoCis webserver is freely accessible at https://powcs.med.unsw.edu.au/OncoCis/.

Edit the Genome

Bioinformatics Post Doctoral Position

http://www.genomeweb.com/node/1435336

Perl Code for Optical alignment

# Needleman-Wunsch  Algorithm 

# usage statement
die "usage: $0 <sequence 1> <sequence 2>\n" unless @ARGV == 2;

# get sequences from command line
my ($seq1, $seq2) = @ARGV;

# scoring scheme
my $MATCH    =  1; # +1 for letters that match
my $MISMATCH = -1; # -1 for letters that mismatch
my $GAP      = -1; # -1 for any gap

# initialization
my @matrix;
$matrix[0][0]{score}   = 0;
$matrix[0][0]{pointer} = "none";
for(my $j = 1; $j <= length($seq1); $j++) {
    $matrix[0][$j]{score}   = $GAP * $j;
    $matrix[0][$j]{pointer} = "left";
}
for (my $i = 1; $i <= length($seq2); $i++) {
    $matrix[$i][0]{score}   = $GAP * $i;
    $matrix[$i][0]{pointer} = "up";
}

# fill
for(my $i = 1; $i <= length($seq2); $i++) {
    for(my $j = 1; $j <= length($seq1); $j++) {
        my ($diagonal_score, $left_score, $up_score);

        # calculate match score
        my $letter1 = substr($seq1, $j-1, 1);
        my $letter2 = substr($seq2, $i-1, 1);                            
        if ($letter1 eq $letter2) {
            $diagonal_score = $matrix[$i-1][$j-1]{score} + $MATCH;
        }
        else {
            $diagonal_score = $matrix[$i-1][$j-1]{score} + $MISMATCH;
        }

        # calculate gap scores
        $up_score   = $matrix[$i-1][$j]{score} + $GAP;
        $left_score = $matrix[$i][$j-1]{score} + $GAP;

        # choose best score
        if ($diagonal_score >= $up_score) {
            if ($diagonal_score >= $left_score) {
                $matrix[$i][$j]{score}   = $diagonal_score;
                $matrix[$i][$j]{pointer} = "diagonal";
            }
        else {
                $matrix[$i][$j]{score}   = $left_score;
                $matrix[$i][$j]{pointer} = "left";
            }
        } else {
            if ($up_score >= $left_score) {
                $matrix[$i][$j]{score}   = $up_score;
                $matrix[$i][$j]{pointer} = "up";
            }
            else {
                $matrix[$i][$j]{score}   = $left_score;
                $matrix[$i][$j]{pointer} = "left";
            }
        }
    }
}

# trace-back

my $align1 = "";
my $align2 = "";

# start at last cell of matrix
my $j = length($seq1);
my $i = length($seq2);

while (1) {
    last if $matrix[$i][$j]{pointer} eq "none"; # ends at first cell of matrix

    if ($matrix[$i][$j]{pointer} eq "diagonal") {
        $align1 .= substr($seq1, $j-1, 1);
        $align2 .= substr($seq2, $i-1, 1);
        $i--;
        $j--;
    }
    elsif ($matrix[$i][$j]{pointer} eq "left") {
        $align1 .= substr($seq1, $j-1, 1);
        $align2 .= "-";
        $j--;
    }
    elsif ($matrix[$i][$j]{pointer} eq "up") {
        $align1 .= "-";
        $align2 .= substr($seq2, $i-1, 1);
        $i--;
    }    
}

$align1 = reverse $align1;
$align2 = reverse $align2;
print "$align1\n";
print "$align2\n";

Needleman- Wunsch explained in detail

Global alignment algorithm:

The alignment takes place in a two-dimensional matrix in which each cell corresponds to a pairing of one letter from each sequence.The alignment starts at the upper left and follows a mostly diagonal path down and to the right. When two letters are aligned, the path follows a diagonal trajectory. There are several places in which the letters from TCGCA are paired to gap characters. In this case, the graph is followed horizontally. Although not shown here, the path may be also be followed vertically when the letters from TCCA are paired with gap characters. Gap characters can never be paired to each other.

 Initialization:
In the initialization phase, you assign values for the first row and column . The next stage of the algorithm depends on this. The score of each cell is set to the gap score multiplied by the distance from the origin. Gaps may be present at the beginning of either sequence, and their cost is the same as anywhere else. The arrows all point back to the origin, which ensures that alignments go all the way back to the origin (a requirement for global alignment). If the gap penalty is -2 then it'll be -2, -4,-6...

 Fill :
In the fill phase (also called induction), the entire matrix is filled with scores and pointers using a simple operation that requires the scores from the diagonal, vertical, and horizontal neighbouring cells. You will compute three scores: a match score, a vertical gap score, and a horizontal gap score. The match score is the sum of the diagonal cell score and the score for a match (+1 or -1). The horizontal gap score is the sum of the cell to the left and the gap score (-1), and the vertical gap score is computed analogously. Once you've computed these scores, assign the maximum value to the cell and point the arrow in the direction of the maximum score. Continue this operation until the entire matrix is filled, and each cell contains the score and pointer to the best possible alignment at that point.
The match score is the sum of the preceding diagonal cell (score = 0) and the score for aligning C to P (-1). The total match score is -1. The horizontal gap score is the sum of the score to the left (-1) and the gap score (-1). The horizontal gap score is therefore -2. The same is true for the vertical gap score. Your maximum score is therefore the diagonal score (-1), and the pointer is set to the diagonal

 Trace back :

The trace-back lets you recover the alignment from the matrix. Like the other parts of this algorithm, it's pretty simple. Start at the bottom-right corner and follow the arrows until you get to the beginning. To produce the alignment, at each cell, write out the corresponding letters or a hyphen for the gap symbol. Since you're following it from the end to the start, the alignment will be backward, and you just reverse it. The final alignment looks like this: