Dr Guillaume Charbonnier

ATAC-Seq Analysis

Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) using Illumina short reads is a powerful technique for analyzing chromatin accessibility across the genome. This method involves using a transposase enzyme to insert sequencing adapters into open chromatin regions, sequencing the DNA fragments, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from ATAC-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome.
• Peak calling: Identifying regions of the genome with accessible chromatin.
• Differential accessibility analysis: Comparing chromatin accessibility patterns between conditions.
• Functional annotation: Annotating peaks with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining ATAC-Seq data with other types of data (e.g., RNA-Seq, ChIP-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of ATAC-Seq data and results.

Bionano Analysis

Bionano optical genome mapping (OGM) is a high-resolution molecular technique that enables the visualization and analysis of large-scale genomic structures. This method utilizes ultra-high molecular weight DNA molecules labeled at specific sequence motifs, which are then linearized and imaged through nanochannel arrays. The resulting optical maps provide long-range genomic information, allowing for the detection of structural variants (SVs) such as deletions, insertions, inversions, and translocations, as well as copy number variations (CNVs). OGM offers advantages over traditional sequencing-based methods, particularly in resolving complex genomic regions and repetitive sequences that are challenging for short-read technologies.

Bioinformatics services I can provide from Bionano OGM data include:

• De novo genome assembly and scaffolding
• Structural variant detection and analysis
• Copy number variation identification
• Genome-wide SV profiling and annotation
• Integration of OGM data with sequencing data for hybrid assemblies
• Comparative genomic analysis across multiple samples
• Custom pipeline development for specific research questions
• Quality control and data preprocessing of OGM raw data
• Visualization of genomic structures and SVs
• Statistical analysis of structural genomic features

ChIP-Seq Analysis

Chromatin Immunoprecipitation Sequencing (ChIP-Seq) for Histone Marks using Illumina short reads is a powerful technique for analyzing protein-DNA interactions and identifying histone modifications across the genome. This method involves immunoprecipitating DNA-protein complexes, sequencing the DNA fragments, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from ChIP-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome.
• Peak calling: Identifying regions of the genome enriched with histone modifications.
• Differential binding analysis: Comparing histone modification patterns between conditions.
• Functional annotation: Annotating peaks with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining ChIP-Seq data with other types of data (e.g., RNA-Seq, ATAC-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of ChIP-Seq data and results.

ChIP-Seq TF Analysis

Chromatin Immunoprecipitation Sequencing (ChIP-Seq) for Transcription Factors using Illumina short reads is a powerful technique for analyzing protein-DNA interactions and identifying transcription factor binding sites across the genome. This method involves immunoprecipitating DNA-protein complexes, sequencing the DNA fragments, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from ChIP-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome.
• Peak calling: Identifying regions of the genome enriched with transcription factor binding sites.
• Differential binding analysis: Comparing transcription factor binding patterns between conditions.
• Motif analysis: Identifying DNA motifs recognized by transcription factors.
• Functional annotation: Annotating peaks with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining ChIP-Seq data with other types of data (e.g., RNA-Seq, ATAC-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of ChIP-Seq data and results.

Chromatin State Analysis

Chromatin segmentation analysis is a powerful tool for understanding the functional organization of the genome. This advanced bioinformatics service utilizes histone modification ChIP-seq data to identify distinct chromatin states across the genome, providing crucial insights into gene regulation and genomic function. By employing robust computational methods, the genome is segmented into regions with similar histone mark patterns, revealing key functional elements such as active promoters, enhancers, repressed regions, and transcriptionally active gene bodies.

The integration of multiple histone modifications creates a comprehensive map of chromatin states, offering a nuanced view of the regulatory landscape. This valuable analysis is particularly beneficial for researchers studying epigenetic mechanisms, gene expression patterns, or seeking to annotate functional elements in their genomic data. Whether applied to basic research or applied genomics, chromatin segmentation analysis can provide essential insights to guide projects forward and unlock new understanding of genome function.

For researchers and organizations seeking to leverage this powerful approach, expert bioinformatics support is available to design, execute, and interpret chromatin segmentation analyses tailored to specific research questions and datasets.

CITE-Seq Analysis

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a powerful technique that combines single-cell RNA sequencing (scRNA-Seq) with protein marker detection, providing insights into both gene expression and protein abundance at the single-cell level. This method involves labeling cells with oligonucleotide-tagged antibodies, sequencing the RNA and antibody tags, and mapping the sequences to a reference genome or transcriptome.

Bioinformatics services I can provide from CITE-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome or transcriptome.
• Cell clustering: Grouping cells based on their gene expression and protein marker profiles.
• Differential expression analysis: Identifying genes and proteins with significant changes in expression between cell clusters or conditions.
• Cell type identification: Annotating cell clusters with known cell types based on marker genes and proteins.
• Trajectory analysis: Inferring developmental lineages and cell state transitions.
• Integration with other omics data: Combining CITE-Seq data with other types of data (e.g., proteomics, epigenomics) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of single-cell data and results.

Differential Expression Analysis

RNA-Seq differential expression analysis is a powerful tool for researchers seeking to understand gene activity changes across different biological conditions. This sophisticated method allows for the identification of genes that are significantly up- or down-regulated between experimental groups, providing crucial insights into cellular processes, disease mechanisms, and treatment responses. By leveraging advanced statistical techniques and robust bioinformatics pipelines, this analysis transforms raw sequencing data into actionable biological knowledge. Whether you're investigating a novel drug's effect on gene expression, exploring developmental stages in an organism, or unraveling the complexities of a disease state, a well-executed RNA-Seq differential expression analysis can reveal key molecular players and potential therapeutic targets. This service offers a comprehensive approach, from initial quality control of sequencing data to the final delivery of clear, interpretable results, ensuring that researchers can make informed decisions and advance their scientific inquiries with confidence.

EM-Seq Analysis

Enzymatic Methyl-seq (EM-Seq) using Illumina short reads is a novel method for analyzing DNA methylation at single-base resolution without the harsh treatment of bisulfite conversion. This technique involves enzymatic conversion of unmethylated cytosines to uracil, sequencing the treated DNA, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from EM-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping enzymatically treated reads to a reference genome.
• Methylation calling: Identifying methylated cytosines and quantifying methylation levels.
• Differential methylation analysis: Comparing methylation patterns between conditions.
• Functional annotation: Annotating differentially methylated regions with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining EM-Seq data with other types of data (e.g., RNA-Seq, ChIP-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of methylation data and results.

Related publication:

• Jean-Pierre R, Roperch J, Charbonnier G, Figiel S, Lamb A, Mills I, Hennion C, Cancel-Tassin G, Cussenot O.
  Multiple Highly Methylated CpG Sites as Potential Epigenetic Markers for the Diagnosis of Prostate Cancer. MDPI AG. 2024 Dec. doi: 10.20944/preprints202412.2005.v1

GSEA

Gene Set Enrichment Analysis (GSEA) is a powerful computational method used in bioinformatics to interpret large-scale genomic data, particularly gene expression profiles. This approach shifts the focus from individual genes to groups of genes that share common biological functions, pathways, or regulatory mechanisms. GSEA evaluates whether a predefined set of genes shows statistically significant, cumulative changes in expression that correlate with specific phenotypes or experimental conditions. By analyzing the collective behavior of gene sets rather than isolated genes, GSEA provides a more robust and biologically meaningful interpretation of complex genomic data. This method is particularly valuable in identifying subtle but coordinated changes in gene expression that may be missed by traditional single-gene analysis approaches. GSEA has found wide applications in various fields, including cancer research, drug discovery, and functional genomics, offering insights into underlying biological processes and potential therapeutic targets. GSEA are usually applied locally using clusterProfiler .

HiC Analysis

Hi-C (High-throughput Chromosome Conformation Capture) analysis is a powerful bioinformatics service that enables researchers to map genome-wide chromatin interactions and elucidate the three-dimensional organization of chromosomes. This technique involves cross-linking chromatin, fragmenting DNA with restriction enzymes, ligating biotinylated ends, and sequencing the resulting DNA fragments. The bioinformatics pipeline for Hi-C data analysis typically includes quality assessment, alignment to reference genomes, filtering, bias correction, and construction of genome-wide interaction matrices. Advanced analysis can reveal important chromatin features such as topologically associating domains (TADs), A/B compartments, and long-range regulatory interactions.

Bioinformatics services I can provide from Hi-C data include:

• Quality assessment of Hi-C data
• Alignment to reference genomes
• Filtering and bias correction
• Construction of genome-wide interaction matrices
• Detection of topologically associating domains (TADs)
• Identification of A/B compartments
• Analysis of long-range regulatory interactions
• Integration with other genomic and epigenomic datasets (e.g., RNA-Seq, ChIP-Seq, ATAC-Seq)
• Custom pipeline development for specific research questions
• Visualization of chromatin interactions and features
• Statistical analysis of chromatin dynamics and genome organization

WGS Analysis

Whole Genome Sequencing (WGS) using Illumina short reads is a comprehensive method for analyzing the entire genome at high resolution. This technique involves fragmenting the DNA, sequencing the fragments, and assembling the sequences to reconstruct the genome. WGS provides detailed information on genetic variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants.

Bioinformatics services I can provide from WGS data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Genome assembly: Assembling short reads into contiguous sequences (contigs) and scaffolds.
• Variant calling: Identifying SNPs, insertions, deletions, and structural variants.
• Annotation: Annotating genetic variants with functional information.
• Comparative genomics: Comparing genomes across different samples or species.
• Population genomics: Analyzing genetic diversity and population structure.
• Phylogenetic analysis: Constructing phylogenetic trees to study evolutionary relationships.
• Integration with other omics data: Combining WGS data with transcriptomics, proteomics, or epigenomics data for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of genomic data and variants.

Multi-omics Data Integration

Multiomics data integration, particularly focusing on RNA-seq, ChIP-seq, and DNA methylation data, offers a comprehensive approach to understanding complex biological systems and regulatory mechanisms. This advanced bioinformatics service combines multiple layers of genomic information to provide a holistic view of gene regulation and cellular function.

By integrating RNA-seq, ChIP-seq, and DNA methylation data, researchers can:

• Identify correlations between gene expression patterns and epigenetic modifications
• Uncover regulatory networks involving transcription factors and histone modifications
• Reveal the impact of DNA methylation on gene expression and chromatin structure

This integrative analysis enables the discovery of intricate relationships between different molecular layers, offering insights that may not be apparent when analyzing each dataset in isolation. For example, it can reveal how specific histone modifications or DNA methylation patterns correlate with gene expression levels, or how transcription factor binding sites relate to nearby gene activity.

Advanced computational methods, such as multivariate analysis techniques and machine learning algorithms, are employed to handle the high dimensionality and complexity of multiomics datasets. These methods can identify common sources of variation across different data types and highlight key features that distinguish between biological conditions or sample groups.

The power of multiomics integration lies in its ability to:

• Provide a more complete picture of cellular processes
• Identify potential biomarkers with higher accuracy
• Reveal novel regulatory mechanisms
• Support more robust hypotheses for further experimental validation

For researchers and organizations seeking to leverage the full potential of their genomic data, expert bioinformatics support is available to design, execute, and interpret multiomics integration analyses tailored to specific research questions and datasets. This approach can significantly enhance the depth and breadth of insights gained from genomic studies, potentially leading to breakthroughs in understanding disease mechanisms, drug responses, and fundamental biology.

Interactome Analysis

Cell-Cell Interaction Analysis from RNA sequencing data has emerged as a powerful approach to elucidate the complex network of cellular communications within biological systems. This method leverages high-throughput transcriptomic data to infer potential ligand-receptor interactions between different cell types, providing insights into the intricate crosstalk that underlies various physiological and pathological processes. By integrating information on known ligand-receptor pairs with gene expression profiles, researchers can predict and quantify intercellular signaling events across diverse cell populations. This approach is particularly valuable for understanding the tumor microenvironment, immune responses, and developmental processes, where cell-cell communication plays a crucial role. The analysis typically involves identifying expressed ligands and receptors in distinct cell types, calculating the likelihood of their interactions, and assessing the statistical significance of these predicted communications. Advanced computational methods can further refine these predictions by considering the subunit architecture of protein complexes and incorporating spatial information when available. By revealing the molecular basis of cellular crosstalk, interactome analysis from RNA-seq data provides a comprehensive view of tissue function and offers potential targets for therapeutic interventions.

Isoform Quantification

Isoform quantification is a critical process in transcriptomics that aims to accurately measure the expression levels of different transcript variants originating from the same gene. This task is essential for understanding gene regulation, functional diversity, and cellular behavior at a more granular level than gene-level quantification. The process involves estimating the abundance of both annotated and novel isoforms, including those resulting from alternative splicing, alternative transcription start sites, or alternative polyadenylation. Quantification methods have evolved to accommodate different sequencing technologies, each with its own strengths and limitations. Short-read sequencing, such as Illumina, provides high depth but faces challenges in deconvoluting complex isoform structures due to limited read length. In contrast, long-read technologies like Oxford Nanopore offer full-length transcript sequencing, enabling better resolution of isoform diversity and facilitating the discovery of novel isoforms. However, long-read sequencing typically yields lower depth and higher error rates, necessitating specialized analytical approaches. Advanced computational methods, such as expectation-maximization algorithms and probabilistic frameworks, have been developed to address technology-specific biases and improve the accuracy of isoform quantification across platforms.

MethylationEPIC Analysis

The Infinium MethylationEPIC BeadChip is a high-throughput DNA methylation profiling platform that enables comprehensive genome-wide analysis of methylation patterns at single-nucleotide resolution. It interrogates over 850,000 methylation sites per sample, covering CpG islands, genes, enhancers, and other regulatory regions.

Bioinformatics analyses of MethylationEPIC array data typically involve several key steps:

• Data preprocessing: This includes quality control, filtering, normalization, and batch effect correction.
• Unsupervised classification: Clustering samples based on their methylation profiles to discover novel subgroups or patterns without prior knowledge of sample labels.
• Differential methylation analysis: Identifying differentially methylated positions (DMPs) and preset regions (DMRs) between sample groups.
• Functional analysis: Annotating methylation sites to genomic features and performing pathway enrichment analyses.
• Cell type deconvolution: Estimating the proportions of different cell types in a sample based on its methylation profile.
• Supervised classification: Using machine learning algorithms to classify samples based on their methylation profiles, which can help in identifying disease subtypes or predicting clinical outcomes.
• Feature selection: Identifying the most informative methylation sites for classification, enabling the design of biomarker panels for targeted methods such as MS-MLPA.
• Integration with other omics data: Combining methylation data with gene expression or other epigenetic marks for comprehensive epigenomic profiling.
• Copy number variation (CNV) analysis: Inferring CNVs from methylation array data using specialized tools like Conumee.

MNase-Seq Analysis

Micrococcal Nuclease Sequencing (MNase-Seq) using Illumina short reads is a powerful technique for analyzing chromatin accessibility and nucleosome positioning across the genome. This method involves digesting chromatin with micrococcal nuclease, sequencing the resulting DNA fragments, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from MNase-Seq data include:

• Data preprocessing: Quality control , filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome.
• Nucleosome positioning: Identifying nucleosome-bound regions of the genome.
• Differential accessibility analysis: Comparing chromatin accessibility patterns between conditions.
• Functional annotation: Annotating nucleosome-bound regions with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining MNase-Seq data with other types of data (e.g., RNA-Seq, ChIP-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of MNase-Seq data and results.

Related publication

• Shiota H, Barral S, Buchou T, Tan M, Couté Y, Charbonnier G, Reynoird N, Boussouar F, Gérard M, Zhu M, Bargier L, Puthier D, Chuffart F, Bourova-Flin E, Picaud S, Filippakopoulos P, Goudarzi A, Ibrahim Z, Panne D, Rousseaux S, Zhao Y, Khochbin S.
  Nut Directs p300-Dependent, Genome-Wide H4 Hyperacetylation in Male Germ Cells. Cell Reports. 2018 Sep;24(13):3477-3487.e6. doi: 10.1016/j.celrep.2018.08.069

Nanopore WGS Analysis

Long-read whole genome sequencing (WGS) using Oxford Nanopore Technologies offers a powerful approach for integrated genomic and epigenomic analysis. This technology enables the generation of ultra-long reads, often exceeding 10 kb in length, which facilitates improved resolution of complex genomic regions and structural variants. A key advantage of nanopore sequencing is its ability to directly detect DNA modifications, including 5-methylcytosine (5mC), without the need for bisulfite conversion. This capability allows for simultaneous sequencing and methylation analysis, providing a comprehensive view of both the genetic and epigenetic landscape. Bioinformatics services for nanopore long-read WGS typically involve sophisticated algorithms for base-calling, read alignment, and methylation detection. Tools such as Nanopolish, Megalodon, and DeepSignal have demonstrated high performance in accurately calling CpG methylation at single-molecule, single-base resolution. These analytical pipelines can process the ionic current signals generated during nanopore sequencing to infer both DNA sequence and methylation status, enabling the creation of high-resolution methylome maps alongside genomic data. The integration of sequence and methylation information from long reads allows for the analysis of methylation patterns across extended genomic regions, including those with low CpG density, which are challenging to assess with short-read technologies.

Over-Representation Analysis

Over-Representation Analysis (ORA) is a powerful tool in the bioinformatician's arsenal for interpreting high-throughput omics data. This method examines whether specific gene sets or biological functions are statistically overrepresented in a subset of "interesting" genes compared to what would be expected by chance. ORA helps researchers uncover meaningful biological insights from large-scale experiments, such as gene expression microarrays or proteomics studies, by identifying enriched functional categories within gene lists. By leveraging databases like Gene Ontology (GO) or MSigDB, ORA can reveal underlying biological processes, molecular functions, or cellular components that are particularly relevant to the experimental conditions being studied. This approach is especially valuable for researchers seeking to translate raw data into actionable hypotheses, guiding further experimental design or highlighting potential therapeutic targets. With its ability to distill complex datasets into functionally relevant information, ORA serves as a critical bridge between raw experimental results and meaningful biological interpretation. ORA are usually applied through prefilled queries to webservices like Enrichr , and g:Profiler , but could also be computed locally if needed using eg clusterProfiler .

Quality Control using MultiQC

As a seasoned bioinformatician with over a decade of experience, I provide comprehensive quality control (QC) services to ensure the reliability and accuracy of your NGS data analysis. My approach involves systematically evaluating and validating your sequencing data at every critical checkpoint. I employ a multi-tiered QC process that includes:

• Raw Data Assessment:
  • Per-base sequence quality analysis
  • GC content distribution evaluation
  • Overrepresented sequence detection
  • Adapter content quantification
• Alignment Quality Control:
  • Mapping rate optimization
  • Insert size distribution analysis
  • Coverage uniformity assessment
  • Duplicate read quantification
• Post-Processing QC:
  • Variant calling accuracy verification
  • Base quality score recalibration
  • Read depth distribution analysis
  • Strand bias detection

I utilize industry-standard tools like FastQC, Picard, and GATK integrated with custom scripts to generate comprehensive QC metrics. My pipeline culminates in the production of an interactive MultiQC report, offering you a centralized dashboard for efficient data quality evaluation.

This systematic approach enables early detection of anomalies, facilitates informed decision-making in your analysis workflow, and ensures your data meets rigorous quality standards for downstream applications.

By partnering with me for your QC needs, you'll gain peace of mind knowing that your data is thoroughly vetted by an experienced professional who understands the importance of precision in bioinformatics research.

RNA-Seq Analysis

RNA sequencing (RNA-Seq) using Illumina short reads is a powerful technique for analyzing the transcriptome, providing insights into gene expression, alternative splicing, and transcript isoforms. This method involves converting RNA to cDNA, sequencing the cDNA fragments, and mapping the sequences to a reference genome or transcriptome.

Bioinformatics services I can provide from RNA-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome or transcriptome.
• Transcript assembly: Reconstructing transcripts from aligned reads.
• Quantification: Measuring gene and transcript expression levels.
• Differential expression analysis: Identifying genes and transcripts with significant changes in expression between conditions.
• Alternative splicing analysis: Detecting and quantifying alternative splicing events.
• Functional annotation: Annotating genes and transcripts with functional information.
• Pathway analysis: Identifying enriched pathways and biological processes.
• Integration with other omics data: Combining RNA-Seq data with other types of data (e.g., proteomics, epigenomics) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of transcriptomic data and results.

Sample mislabeling analysis

Sample mislabeling analysis is a critical bioinformatics service aimed at ensuring the integrity of high-throughput genomic datasets by identifying and resolving inconsistencies between genotype and sequence data. Utilizing advanced methods such as single nucleotide polymorphism (SNP) analysis, this service detects allelic mismatches that can arise from errors in sample handling or technical biases, such as cross-sample contamination or amplification artifacts. SNP analysis not only validates sample identity but also facilitates downstream applications like quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS), where precision is paramount. Additionally, correlation analysis is employed to uncover relationships between biological variables across datasets, enabling the identification of batch effects or confounding factors. Techniques such as Pearson or canonical correlation analysis are applied to quantify associations, ensuring robust integration of multi-omics data for accurate biological interpretation. Together, these analyses provide a comprehensive framework for addressing sample mislabeling while enhancing data reliability and interpretability.

scRNA-Seq Analysis

Single-cell RNA sequencing (scRNA-Seq) using Illumina short reads is a powerful technique for analyzing the transcriptome at the single-cell level, providing insights into cellular heterogeneity, gene expression, and cell type identification. This method involves isolating individual cells, converting RNA to cDNA, sequencing the cDNA fragments, and mapping the sequences to a reference genome or transcriptome.

Bioinformatics services I can provide from scRNA-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome or transcriptome.
• Cell clustering: Grouping cells based on their gene expression profiles.
• Differential expression analysis: Identifying genes with significant changes in expression between cell clusters or conditions.
• Cell type identification: Annotating cell clusters with known cell types based on marker genes.
• Trajectory analysis: Inferring developmental lineages and cell state transitions.
• Integration with other omics data: Combining scRNA-Seq data with other types of data (e.g., proteomics, epigenomics) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of single-cell data and results.

Cell Type Identification

Cell type identification is a critical step in single-cell RNA sequencing analysis that involves assigning biological identities to clusters of cells based on their gene expression profiles. This process transforms abstract data points into meaningful biological entities, allowing researchers to characterize tissue composition, discover novel cell populations, and track cellular changes in development or disease.

My cell type identification services include:

• Marker-based annotation: Identifying cell types using established marker genes from literature and databases.
• Reference-based classification: Matching cells to reference atlases using supervised learning approaches.
• Automated annotation tools: Employing tools like SingleR, Garnett, or scANVI for systematic cell type assignment.
• Novel cell type discovery: Identifying and characterizing previously undescribed cell populations.
• Cell state discrimination: Distinguishing between different functional states within known cell types.
• Cross-species analysis: Transferring cell type annotations across different species or model organisms.
• Rare cell identification: Detecting and characterizing rare or underrepresented cell populations.
• Confidence assessment: Providing statistical confidence scores for cell type assignments.
• Custom reference building: Creating project-specific cell type references from your data.

Trajectory Analysis

Trajectory analysis is a computational approach for studying cellular differentiation and dynamic biological processes through the lens of single-cell genomics data. This method organizes cells along developmental paths or "trajectories" based on their gene expression patterns, revealing the continuity of cellular states during processes like cell differentiation, immune response, or disease progression.

My trajectory analysis services include:

• Pseudotime inference: Ordering cells along progression trajectories to reconstruct developmental processes.
• Branching analysis: Identifying cellular decision points and bifurcations in developmental pathways.
• RNA velocity analysis: Predicting future cell states based on spliced and unspliced mRNA ratios.
• Gene dynamics modeling: Characterizing expression patterns of genes along cellular trajectories.
• Regulatory network inference: Reconstructing gene regulatory networks driving cell state transitions.
• Integration with perturbation data: Combining trajectory analysis with CRISPR or drug perturbation studies.
• Multi-omics trajectory analysis: Integrating RNA-seq with ATAC-seq or other data types for comprehensive analysis.
• Custom visualization: Creating intuitive visual representations of trajectory data and analytical results.
• Method benchmarking: Comparing multiple trajectory inference methods to identify the most appropriate for your data.

smRNA-Seq Analysis

Small RNA sequencing (smRNA-Seq) using Illumina short reads is a powerful technique for analyzing the small RNA transcriptome, including microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs). This method involves isolating small RNA molecules, converting them to cDNA, sequencing the cDNA fragments, and mapping the sequences to a reference genome or transcriptome.

Bioinformatics services I can provide from smRNA-Seq data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping small RNA reads to a reference genome or transcriptome.
• miRNA identification: Detecting known and novel miRNAs.
• siRNA and piRNA analysis: Identifying and characterizing siRNAs and piRNAs.
• Differential expression analysis: Identifying small RNAs with significant changes in expression between conditions.
• Target prediction: Predicting mRNA targets of small RNAs.
• Functional annotation: Annotating small RNAs with functional information.
• Pathway analysis: Identifying enriched pathways and biological processes.
• Integration with other omics data: Combining smRNA-Seq data with other types of data (e.g., mRNA-Seq, proteomics) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of small RNA data and results.

Related publication:

• Mokhtari A, Ibrahim EC, Gloaguen A, Barrot CC, Cohen D, Derouin M, Vachon H, Charbonnier G, Loriod B, Decraene C, Yalcin I, Marie-Claire C, Etain B, Belzeaux R, Delahaye-Duriez A, Lutz PE.
  Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control study. EBioMedicine. 2025 Mar;113:105569. doi: 10.1016/j.ebiom.2025.105569

SNV and Indel detection

Variant calling analysis is a crucial step in understanding genetic diversity and identifying mutations that may impact health, disease, or traits. This powerful bioinformatics approach sifts through sequencing data to pinpoint differences in DNA sequences compared to a reference genome. By employing sophisticated algorithms and statistical methods, variant calling can detect single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations. This process is essential for a wide range of applications, including personalized medicine, crop improvement, and evolutionary studies. Variant calling services ensure accurate identification and annotation of genetic variants, providing researchers and clinicians with valuable insights to drive their projects forward. Whether you're working with human genomes, plant breeding populations, or microbial communities, variant calling analysis can reveal the genetic basis of phenotypic variation and help guide critical decisions in research and clinical settings.

WES Analysis

Whole Exome Sequencing (WES) using Illumina short reads is a targeted approach for sequencing the protein-coding regions of the genome. This technique involves capturing exonic regions, sequencing the captured DNA, and analyzing the sequences to identify genetic variants. WES provides detailed information on coding variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants.

Bioinformatics services I can provide from WES data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping short reads to a reference genome.
• Variant calling: Identifying SNPs, insertions, deletions, and structural variants.
• Annotation: Annotating genetic variants with functional information.
• Comparative genomics: Comparing exomes across different samples or species.
• Population genomics: Analyzing genetic diversity and population structure.
• Phylogenetic analysis: Constructing phylogenetic trees to study evolutionary relationships.
• Integration with other omics data: Combining WES data with transcriptomics, proteomics, or epigenomics data for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of exomic data and variants.

WGBS Analysis

Whole Genome Bisulfite Sequencing (WGBS) using Illumina short reads is a comprehensive method for analyzing DNA methylation across the entire genome at single-base resolution. This technique involves treating DNA with bisulfite to convert unmethylated cytosines to uracil, sequencing the treated DNA, and mapping the sequences to a reference genome.

Bioinformatics services I can provide from WGBS data include:

• Data preprocessing: Quality control, filtering, and trimming of raw sequencing reads.
• Read alignment: Mapping bisulfite-treated reads to a reference genome.
• Methylation calling: Identifying methylated cytosines and quantifying methylation levels.
• Differential methylation analysis: Comparing methylation patterns between conditions.
• Functional annotation: Annotating differentially methylated regions with genomic features and performing pathway enrichment analyses.
• Integration with other omics data: Combining WGBS data with other types of data (e.g., RNA-Seq, ChIP-Seq) for multi-omics analysis.
• Custom pipeline development: Developing tailored bioinformatics pipelines for specific research questions.
• Visualization: Creating visual representations of methylation data and results.