iOMICS - A Cloud based plarform for sequence data managament and analysis.
iOMICS is democratizing sequence analysis - by giving the biologists the ability to do analysis on their own.
Sequencing technologies today has shrunk the time line of performing a wet-lab experiment from years to days and has reduced the cost of sequencing from millions of dollars to a few thousands; the flip-side to this boon is the amount of data it generates for analysis and knowledge discovery. Thanks to deep computing technologies such as High Performance Computing, Cloud Computing and with the advent of high performance accelerated algorithms this herculean task can be made miniscule.
To mitigate the problem of processing, decoding, analyzing and visualizing the NGS data we have developed for the first time in India a Cloud based NGS analysis workflow framework called iOMICS.
iOMICS is a perfect choice for managing, analyzing and visualizing Microarray and NGS data. As a unified and integrated platform It comes with workflow frameworks for analyzing genomics, transcriptomics, metagenomics and epigenetic data. Poised to bring the power of a supercomputer accessible through a laptop or a mobile device, iOMICS is first of its kind to integrate Biology, Distributed computing and Cloud computing in a true sense.
Features
| de novo Assembly | Exome-Seq Analysis | Variation Analysis |
| Quality Assessment | Quality Assessment | Quality Assessment |
| Data Cleaning | Data Cleaning | Data Cleaning |
| Genome Assembly (Generate Contigs and Super-contigs) | Sequence Alignment with Reference genome | Sequence Alignment with Reference genome |
| Genome Assembly (Generate Scaffolds with mate-pair data) | Local realignment around InDels | Local realignment around InDels |
| SNPs/InDels Calling | SNPs/InDels Calling | |
| Variations to Gene | Variations to Gene | |
| Non-synonymous coding variants | ||
| Synonymous coding variants | ||
| Start gained and Start lost variants | ||
| Stop gained and Stop lost variants | ||
| Frame shift caused variants | ||
| Splice site acceptor and donor variants |
| de novo Transcriptome Analysis | Reference Transcriptome Analysis | smallRNA (miRNA) Analysis |
| Quality Assessment | Quality Assessment | Quality Assessment |
| Data Cleaning | Data Cleaning | Data Cleaning |
| Transcript Assembly | Splice junctions identification between exons | smallRNA reads mapping to Reference genome |
| Conserved, Putative Gene prediction and Annotation | Transcript Assembly | Identification of miRNAs |
| Identification of alternative spliced transcripts | Prediction of mi, mi*, and precursor sequences | |
| Conserved, Putative Gene prediction and Annotation | miRNA Target prediction | |
| miRNA Conservation study |
| ChIP-Seq Analysis |
| Quality Assessment |
| Data Cleaning |
| Sequence Alignment with Reference genome |
| Genome-wide scanning to identify enriched regions |
| Peak statistics |
| Summit locations for Peaks |
| Negative Peaks |
| Fold enrichment ranges |
| Functional annotation of Peaks across the genome |
For more information please visit www.genomecomputingcenter.com or write to This e-mail address is being protected from spambots. You need JavaScript enabled to view it.