Next Generation Sequencing

Stratalycs offer complete cutting edge solutions for Next Generation sequencing which include sample library preparation to sequencing to bioinformatics analysis. The ability to generate large volumes of data has helped genetic, biological and medical research. We ensure to give high accuracy and quality data at every step of the process. We offer Clinical services from sequencing to high end Data analysis which include drug and protein interaction study which could help clinician to get the exact targets. Our sequencing services provides different module of analysis whether it be Resequencing, Denovo and Assembly. These modules are applied in different sectors like AgriGenomics, Plant Genome , Bacterial Genomics and Host pathogen interactions. We have well developed expertise in metagenome analysis, plant genomics, animal genomics, human genomics diagnostic etc. Get your data sequence to find out in-depth any therapeutic relation with genome. Stratalycs looks forward to work with you and assist you in achieving your research goals. NGS Platforms available at Stratalycs:
  • Illumina NextSeq500
  • Illumina HiSeq
  • Illumina MiSeq
Platform Number of reads Read Type Application
Illumina Miseq 2 X 150 bp PE Bacterial , Fungal , Metagenomics
Illumina Hiseq 250000000 PE Agriculture , Clinical , Veterinary , Plants, Human
Illumina HiSeq X Ten 375000000 PE Agriculture , Clinical , Veterinary , Plants, Human
Illumina NextSeq 500 400000000 PE Agriculture , Clinical , Veterinary , Plants, Human

Stratalycs provides you with solutions for streamlining for NGS (Next Generation Sequencing) data analysis through effective data management, appropriate analytical strategies and visualization. We are in continuous touch with our customers and we can support requests for personalized and tailored data analysis. Stratalycs provides services for the entire spectrum of NGS data analysis (RNA-Seq, small RNA-Seq, DNA-Seq, Methyl-Seq and ChIP-Seq). Our services are suitable for biologists with specific questions in bioinformatics, and we can integrate your genomics data with other omic and clinical datasets.

Exome & Targeted Resequencing

Exome sequencing is a cost-effective approach when whole-genome sequencing is not practical or necessary. Exome sequencing is an efficient strategy for reading the parts of the genome that are believed to be the most important for diagnosing diseases. Most disease causing variants lie within the exonic regions, splice sites, or promotor regions. These regions comprise about 2% of the human genome. Sequencing the exome provides a cheaper and faster analysis of these regions compared to whole genome sequencing. Sequencing only the coding regions of the genome enables researcher and clinician to get the details on the genes which most likely affect phenotype. Exome sequencing detects variants in coding exons, with the capability to expand targeted content to include untranslated regions (UTRs) and microRNA for a more comprehensive view of gene regulation. DNA libraries can be prepared in as little as 1 day and require only 4–5 Gb of sequencing per exome.


The accurate estimation of differentially expressed genes (DEGs) between disease condition or certain specific conditions is a key in the understanding phenotypic variation. To understand the insight into the transcriptome of a cell there is a need for the high accuracy data which is achieved by High throughput sequencing (RNA-Seq). RNA-Seq describes the estimation of abundance and sequence of RNA transcripts. Which is by far the most abundantly cited NGS method. It uses the capabilities of high throughput sequencing. The method has overcome traditional use of serial analysis of gene expression (SAGE). Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome Thus the methods and algorithm is rapidly increasing. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. Pros:
  • Highly suitable for discovering novel exons, genes, and splice isoforms
  • High dynamic range in expression analysis, compared to microarrays
  • Primer bias
  • Reverse transcriptase may introduce sequencing errors