Computational data analysis

Benefit description

Among the computational data analysis services offered, we provide: - Selection of preprocessing and analysis methods: We identify the appropriate preprocessing and data analysis tools and methods based on the type of sequencing being conducted. - Quality Control: We define strategies for sequencing data quality control, including the removal of low-quality sequences, artifact detection, and coverage assessment. - Computational analysis of High-Throughput Sequencing data (NGS): Our services cover a range of analyses including resequencing (DNA-seq), de novo assembly, RNA-seq, ChIP-seq, ATAC-seq, CLIP-seq, Exome-seq, Ribo-seq, IsoSeq, single-cell analysis, metabolomics, 16S, 18S, ITS metagenomics, and other types of amplicon and shotgun sequencing. We perform pathway enrichment analysis, identification of germline or somatic variants, SNP/InDel analysis, CNV, and structural variants, as well as microarray analyses. We also provide customized graphical representations of results tailored to user requirements. - Computational analysis of proteomics and metabolomics Data: We offer multivariate statistical analysis of proteomics and metabolomics data, including principal component analysis (PCA) or linear discriminant analysis (LDA) for sample clustering based on the expression of specific proteins/metabolites. We map proteins/metabolites onto known metabolic pathways to analyze differential profiles. Customized graphical representation of results is provided in accordance with user requirements. - Analysis of multi-omics data integration: We combine omics data from NGS, such as genomics and transcriptomics, with metabolomics and proteomics data for a comprehensive understanding of biological processes. Machine learning analysis is used to build predictive models. Results are graphically represented in a personalized manner according to user specifications. - Development and implementation custom software for data analysis: We offer programming services in various languages tailored to the needs of our service (Python, R, Bash, Tcsh, etc.) to develop ad hoc scripts. - Statistical analysis of experimental data: We conduct exploratory analysis, hypothesis testing using parametric and non-parametric methods, robust statistical methods, ANOVA, principal component analysis (PCA), data mining (machine learning), ROC curves, and more. We provide customized graphical representation of results according to user specifications. - Sequences Submission to european repositories: We handle the submission of sequences to the European Nucleotide Archive (ENA) and the European Genome-phenome Archive (EGA) under the European Bioinformatics Institute (EBI). This provides a secure and confidential way to maintain data, which is also a requirement for publishing results in scientific journals. As collaborating users of ENA (Brokers), we are responsible for data maintenance and ensuring data privacy renewal.