Bioinformatics Group, Institute of Molecular Biology NAS RA
10.0/10 Overall Rating
100% Positive
3 Orders Completed
Yerevan, AM
We develop and provide creative solutions to high-throughput biological data analysis. Our lab members are experienced in various aspects of bioinformatics and computational biology: starting from raw data processing, ending with systems biology level analysis, as well as with bioinformatics software development. Our group is engaged in research projects aimed at secondary analyses of publicly available datasets to reveal the mechanisms of the development of complex human diseases. On one hand, we develop novel algorithms, approaches, and software for efficient knowledge inference from genomics, transcriptomics, and epigenomics. On the other hand, we use existing tools to perform actual biological research.
Another research direction at focus is telomere bioinformatics: development and application of novel computational approaches for analysis of telomere length dynamics and telomere maintenance mechanisms from gene expression/DNA sequencing data. These and other interests of our group are reflected in the following publications and described at our website (http://big.sci.am/).
Selected publications:
1. Arakelyan A, Nersisyan L, Nikoghosyan M, et al. Transcriptome-Guided Drug Repositioning. Pharmaceutics. 2019 Dec 12;11(12). pii: E677.
2. Nersisyan L, Nikoghosyan M, Arakelyan A; Genome of the Netherlands consortium. WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene. Sci Rep. 2019 Dec 10;9(1):18758.
3. Nersisyan L, Hopp L, Loeffler-Wirth H, et al. Telomere Length Maintenance and Its Transcriptional Regulation in Lynch Syndrome and Sporadic Colorectal Carcinoma. Front Oncol. 2019 Nov 5;9:1172.
4. Loeffler-Wirth H, Kreuz M, Hopp L, Arakelyan A, et al. A modular transcriptome map of mature B cell lymphomas. Genome Med. 2019 Apr 30;11(1):27.
5. Nikoghosyan M, Hakobyan S, Hovhannisyan A, et al. Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach. Front Genet. 2019 Apr 26;10:394.
6. Hopp L, Loeffler-Wirth H, Nersisyan L, et al. Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome. Front Immunol. 2018 Jul 17;9:1620.
7. Arakelyan A, Nersisyan L, Poghosyan D, et al. Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles. PLoS One. 2017 Nov 3;12(11):e0187572. Arakelyan A, Nersisyan L, Petrek M, et al. Cartography of pathway signal perturbations identifies distinct molecular pathomechanisms in malignant and chronic lung diseases. Front. Genet. 2016, 7:79.
8. Hakobyan A, Nersisyan L, Arakelyan A. Quantitative trait association study for mean telomere length in the South Asian Genomes. Bioinformatics 2016, 32(11):1697-700.
9. Nersisyan L, Johnson G, Riel-Mehan M et al. PSFC: a Pathway Signal Flow Calculator App for Cytoscape F1000Research 2015, 4:480.
10. Nersisyan L, Arakelyan A. Computel: computation of mean telomere length from whole-genome next-generation sequencing data. PLoS One 2015, 10(4):e0125201.
11. Nersisyan L, Samsonyan R, Arakelyan A. CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows. Version 2. F1000Res 2014, 3:145.
12. Arakelyan A, Nersisyan L. KEGGParser: parsing and editing KEGG pathway maps in Matlab. Bioinformatics 2013 Feb 15;29(4):518-9.
We provide a wide range of bioinformatics data analysis pipelines. For details, see the available specialized services. If none are relevant, you may contact us through this service.
We have considerable experience in systems biology analysis of high-throughput data, including enrichment/overreperesntation analysis for pathways and functional gene sets, as well as advanced pathway analysis, machine learning approaches and others. If none of the more specialized services correspond to the type of systems biology analyses you'd like to undertake, contact us through this one.
We provide a wide range of biostatistics and bioinformatics data analysis pipelines. For details, see the available specialized services. If none are relevant, you may contact us through this service.
Brainstorming/miscellaneous requests
This service is for the cases where:
Biostatistics/Machine-learning
Input: Any spreadsheet type file with data.
Output: Results are presented in graphical format with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Input: Fastq or BAM raw data files or already processed RPKM/TPM/FPKM files.
Output: Results are presented in graphical format, with clear, comprehensive and esthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Input: CEL and TXT formatted raw data files or Gene Expression Omnibus dataset file formats
Output: Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Advanced pathway analysis
Input: Gene expression matrix (or fold-change values) files in a wide variety of formats (.txt, .csv, .xls, .xlsx). Or no additional input is required, if this is a downstream analysis of microarray and RNA-seq data analysis services.
Output: Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as .gif files showing pathway signal flow in certain cases. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Input: Gene expression matrix (fold-change values) files in a wide variety of formats (.txt, .csv, .xls, .xlsx). Or no additional input is required, if this is a downstream analysis of microarray and RNA-seq data analysis services.
Output: Results are presented in graphical format, with clear, comprehensive and esthetic figures and schemes, as well as .gif files showing pathway signal flow in certain cases. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Computational analysis of telomere biology
Input: Please contact us for data type and format requirements.
Output: Depending on the input data type, computation of mean telomere length (whole genome) or telomere maintenance mechanisms (gene expression data), or association analysis with -omics data from different sources (epigenomics) is possible. Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as .gif files showing telomere maintenance mechanisms’ activity. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Input: fastq or BAM files and phenotype data (optional).
Output: Results are presented in graphical format with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
Input: fastq or BAM files.
Output: For ChIP-seq datasets, the output is details of detected peaks, including genomic position and width of the peak, the summit and the peak score. For BS-seq, the output is positions of all sequenced cytosines (in CpG, CHG and CHH contexts) along with the methylation score. All the results are explained in detail, with the option of further discussions and post-feedback modifications.
"Very proactive, Highly skilled team, Great turnaround time, good communication and willing to jump on call to tweak analysis plan."
"The team at the Institute of Molecular Biology NAS RA provided excellent bioinformatics analysis for us. They recommended an approach that was unique to us among the service providers that we engaged with previously inside and outside the Science Exchange, and ultimately their methods delivered novel insights and leads on biological mechanism. The team was responsive, professional, timely, and thorough in the delivery of the work."
The BIG team at the Institute of Molecular Biology NAS RA, is a great group to work with. They are extremely knowledgeable about the subject matter and biological data. They are very responsive even with the different time zones. Great communicators via zoom, or email and they work to make sure the project is done complete and thorough. Lastly, they adhere to timelines they put in their quotes and typically meet or beat said timelines. I highly recommend this group.