Bioinformatics

Bulk RNA-seq data analysis involves quality control of the raw sequencing data to ensure accuracy and reliability. This is followed by alignment of the reads to a reference genome or transcriptome to map the RNA sequences. Next, the data are processed to quantify gene expression levels. Differential expression analysis is then performed to identify genes that are significantly up- or down-regulated across experimental conditions. A final report will be provided that includes a brief discussion of the results as well as visualizations such as heatmaps, MA plots, and principal component analysis (PCA) to summarize and explore the results. We will carefully consider batch effects, normalization, and statistical methods to ensure robust and reproducible findings.

Please note that this service does not include functional enrichment or pathway analysis.

Proteomics analysis involves taking the quantitative data output from the proteomics facility and performing essential normalizations and quality control checks. For a typical experiment (e.g., Control vs Treated), we identify differentially abundant proteins and generate a final report summarizing the results. This report includes visualizations such as heatmaps and principal component analysis (PCA) to help explore the data. We carefully account for batch effects, normalization, and statistical methods to ensure reliable and reproducible results. 

Please note that this service does not include functional enrichment or pathway analysis.

Pathway analysis involves several steps to identify relevant biological pathways associated with a set of differentially expressed genes (DEGs) or differentially abundant proteins. First, the features are mapped to known pathways using databases of KEGG, Reactome, or Gene Ontology. Statistical methods are then applied to assess whether any pathways are significantly enriched in the gene set compared to a background reference. Results are presented with visualizations such as pathway diagrams or enrichment plots.

Single-cell RNA-seq data analysis involves two steps to explore gene expression at the individual cell level.

• The first step includes quality control, preprocessing, alignment to genome, and creating the standard cell ranger output, including the raw_feature_bc_matrix (which contains every barcode from the fixed list of known barcode sequences that have at least one read. This includes background and cell-associated barcodes) and the filtered_feature_bc_matrix (which contains only detected cell-associated barcodes). Only for model or well-annotated organisms.
• In the second step after quantification, normalization is applied to account for technical variations and cell-specific biases, followed by dimensionality reduction (e.g., PCA or t-SNE) to identify patterns of gene expression across cells. Clustering is then used to group similar cells into distinct cell types or states, and differential expression analysis identifies genes that are significantly different between clusters. Finally, the results are visualized through heatmaps, dot plots, or trajectory analysis, providing insights into cellular heterogeneity, gene expression dynamics, and potential biological processes.

Please note that this service does not include functional enrichment or pathway analysis.

Please also note that authorship may be warranted if we have provided significant assistance in the analysis of complex data types such as scRNA-seq, where the analysis involves tailored decision-making specific to your project. These types of analyses often require customized approaches and may involve literature review to identify the most appropriate solutions for your unique research needs. In such cases, the intellectual contribution of the core facility in designing, troubleshooting, and executing the analysis should be recognized through authorship.

Microbial community analysis, whether based on 16S rRNA amplicon sequencing or shotgun metagenomics, involves several steps to characterize the composition and diversity of microbial populations. For 16S amplicon sequencing, the process begins with quality control and trimming of raw sequence data, followed by the identification and classification of microbial taxa using reference databases. In shotgun metagenomics, the data are first assembled, and taxonomic profiles are generated by mapping reads to a reference genome database. Both approaches involve alpha and beta diversity analysis to assess microbial richness and community structure, followed by statistical tests to identify differentially abundant taxa between sample groups. Results are visualized through heatmaps, bar plots, and principal coordinate analysis (PCoA), providing insights into the microbial composition and ecological relationships within the samples.

WGS (Whole Genome Sequencing) or WES (Whole Exome Sequencing) DNA variant analysis involves several steps to detect single nucleotide variants (SNVs) from short-read sequencing data. The process begins with quality control of raw sequencing reads to assess data integrity and eliminate low-quality sequences. Next, reads are aligned to a reference genome. After alignment, variant calling is performed to identify potential SNVs, using the GATK algorithm to detect base-level variations. The resulting variants are then filtered based on quality metrics and annotated for functional impact. Results are presented in variant tables, and visualizations such as Manhattan plots and allele frequency distributions are used to further interpret the findings.

NanoString data analysis involves several steps to quantify gene expression using the NanoString platform. The process begins with quality control of raw data to assess the integrity of the measurements and remove any outliers or low-quality samples. Normalization is then applied to correct for technical variation across samples, ensuring accurate gene expression comparisons (proteoDA R package). Following normalization, differential expression analysis is performed to identify genes that are significantly up- or down-regulated between experimental conditions (limma R package). A final report will be provided that includes a brief discussion of the results as well as visualizations such as heatmaps and principal component analysis (PCA) to summarize and explore the results. We will carefully consider batch effects, normalization, and statistical methods to ensure robust and reproducible findings.

Please note that this service does not include functional enrichment or pathway analysis.

• Publication-ready data plots: $50 each, includes one 15-minute meeting for any potential changes to the graph. Additional modification requests will be billed as new services.
• Data deposition: $250 per deposition of next-generation sequencing data (e.g., transcriptomes or genomes) to a public database.
• Analysis of sparsely annotated species/non-model organisms: $250 additional charge per analysis (not per sample).