Gene Expression Data Analysis Services
How Our Gene Expression Analysis Services Can Help You ? Genes encode proteins and proteins dictate cell function. Therefore, the thousands of genes expressed in a particular cell determine what that cell can do. Moreover, each step in the flow of information from DNA to RNA to protein provides the cell with a potential control point for self-regulating its functions by adjusting the amount and type of proteins it manufactures.At any given time, the amount of a particular protein in a cell reflects the balance between that protein’s synthetic and degradative biochemical pathways. On the synthetic side of this balance, recall that protein production starts at transcription (DNA to RNA) and continues with translation (RNA to protein).
Thus, control of these processes plays a critical role in determining what proteins are present in a cell and in what amounts. In addition, the way in which a cell processes its RNA transcripts and newly made proteins also greatly influences protein levels. We at RASA Bioinformatic Lab , provide detailed insight for gene expression analysis including the entire Transcriptome analysis (RNA-Seq analysis) and Exome analysis.
Exome Sequence Analysis
As one of the widely used targeted sequencing method, whole-exome sequencing (WES) has become more and more popular in clinical and basic research. Albeit, the exome (protein-coding regions of the genome) makes up ~1 % of the genome, it contains about 85 % of known disease-related variants (van Dijk E.L. et al, 2014), making whole-exome sequencing a fast and cost-effective alternative to whole genome sequencing (WGS).Whole exome sequencing presents a powerful tool to study rare genetic disorders. The most challenging part of using exome sequencing for the purpose of disease-causing variant detection is analyzing, interpreting, and filtering the large number of detected variants. We at RASA Bioinformatic CRO Lab address strategies in selecting samples to sequence, and technical considerations involved in exome sequencing. RASA Bioinformatic Lab then plans out how to identify variants, and methods for first annotating detected variants using characteristics such as allele frequency, location in the genome, and predicted severity, and then classifying and prioritizing the detected variants based on those annotations. Finally, we at RASA also review possible gene annotations that may help to establish relationship between genes carrying high-priority variants and the phenotype in question, in order to identify the most likely causative mutations.
Transcriptome analysis experiments enable researchers to characterize transcriptional activity (coding and non-coding), focus on a subset of relevant target genes and transcripts, or profile thousands of genes at once to create a global picture of cell function. Gene expression analysis studies can provide a snapshot of actively expressed genes and transcripts under various conditions. Next-generation sequencing (NGS) capabilities have shifted the scope of transcriptomics from the interrogation of a few genes at a time to the profiling of genome-wide gene expression levels in a single experiment. Hence, finding out how to analyze gene expression along with identifying novel transcripts using NGS-based RNA sequencing (RNA-Seq) methods is breakthrough in one of the major applications of NGS technologies.
Essential Steps in Trancriptomic Data analysis done at RASA are as follows; identify pairs of ‘incompatible’ fragments that must have originated from distinct spliced mRNA iso-forms. Fragments are then analysed if connected in an ‘overlap graph’ when they are compatible and their alignments overlap in the genome. The iso-forms are then assembled from the overlap graph (minimal approach) and at last the transcript abundance is estimated in FPKMs (Fragments per Kilobase of exon per Million fragments mapped) to study the expression of a particular gene in specific tissue or organ in a defined condition.