Search Results for "junctions"

QuCAT stands for Quantum Circuit Analyzer Tool. This open source python library provides standard analysis tools for superconducting electronic circuits, built around Josephson junctions. QuCAT features an intuitive graphical or programmatical interface to create circuits, the ability to compute their Hamiltonian, and a set of complimentary functionalities such as calculating dissipation rates or visualizing current flows in the circuit. QuCAT currently supports quantization in the basis of normal modes.

INTEGRATE: Calling gene fusions with exact fusion junctions and genomic breakpoints by combining RNA-Seq and WGS data. To download source code, reference manual and test case, please go to 'Files'. Also refer to 'Wiki' for details.

FineSplice is a Python wrapper to TopHat2 geared towards a reliable identification of expressed exon junctions from RNA-Seq data, at enhanced detection precision with small loss in sensitivity. Following alignment with TopHat2 using known transcript annotations, FineSplice takes as input the resulting BAM file and outputs a confident set of expressed splice junctions with the corresponding read counts. Potential false positives arising from spurious alignments are filtered out via a semi-supervised anomaly detection strategy based on logistic regression. ...

FastAlign.pl provides a more intuitive output to find exon-intron junctions. The query string is in amino acids and the hit string is in nucleotides. There are extra nucleotides at the end of the hit string (option -diff and by default = 10), that allow to verify if the intron start with common rules (5'-GTGCGA-... for group II intron and after an exonic T for group I intron). If you have Emboss, you can genarate a graphic with option -graph 1.

...It requires a BAM alignment file from an RNA-seq experiment and an annotation file in GTF format detailing the location of the exons in the genome. It uses paired end reads where one mate maps to one exon and the other mate to a different exon and/or split reads spanning exon exon junctions to count “links” between two exons. When there are overlapping exons, these are grouped into “exon groups” and unique portions of each exon in an exon group are identified which are used when assigning reads to an exon. The link counts ascertained from unique regions are normalized with the probability of observing such a link given the insert size distribution which is referred to as link coverage. ...