NGS Data Analysis and Bioinformatics

Transform Sequencing Data Into Research-Ready Results

NGS Data Analysis & Bioinformatics provides customized computational workflows for researchers working with next-generation sequencing data. From initial quality control and data processing to advanced analysis, visualization, and reporting, the workflow can be adapted to your sequencing platform, data type, organism, research objective, and desired output.

Gentaur supports research projects involving RNA sequencing, whole-genome sequencing, whole-exome sequencing, targeted sequencing, single-cell sequencing, and other NGS datasets.

Whether you need a complete analysis workflow or support with a specific stage of your project, the analysis can be configured around your research requirements.

Your Data. Your Research Question. Your Analysis

Different sequencing projects require different computational approaches.

Our bioinformatics service can be adapted according to:

1. Sequencing data type
2. Organism and reference genome
3. Experimental design
4. Number of samples
5. Research objectives
6. Required analysis pipeline
7. Desired visualizations
8. Final data format and reporting requirements

NGS Analysis Workflow

01 Project Assessment

Start by defining your sequencing project, research objective, data type, organism, and expected results.

02 Data Submission

Provide your sequencing files and relevant experimental information.

Supported project data may include formats such as:

FASTQ · BAM · SAM · VCF · FASTA

03 Quality Control

Raw sequencing data can be assessed for key quality characteristics before downstream analysis.

Quality control may include:

1. Read quality
2. Sequence quality
3. Adapter content
4. Read duplication
5. GC content
6. Coverage
7. Mapping statistics

04 Data Processing

The appropriate preprocessing and data-processing workflow is selected according to the sequencing project.

This may include:

Read trimming → Filtering → Alignment → Quantification → Variant processing

05 Advanced Analysis

Perform the analysis required for the research question.

Depending on the project, this can include:

1. Differential expression
2. Variant analysis
3. Genome analysis
4. Transcriptome analysis
5. Functional analysis
6. Pathway analysis
7. Single-cell analysis
8. Comparative analysis

06 Visualization

Transform analysis results into clear research-ready visualizations.

Possible outputs include:

Heatmaps · Volcano plots · PCA plots · Coverage plots · Genome views · Clustering plots · Pathway visualizations

07 Results & Reporting

Receive organized analysis results together with appropriate tables, figures, processed datasets, and a structured report describing the workflow and outputs.

NGS Analysis Services

🧬 RNA-Seq Analysis

Analyze transcriptomic sequencing data to investigate gene expression patterns and differences between experimental groups.

Possible analyses include:

1. Quality control
2. Read alignment
3. Transcript quantification
4. Differential expression
5. Functional enrichment
6. Pathway analysis
7. Visualization

🧬 Whole Genome Sequencing

Analyze whole-genome sequencing datasets for genome-wide research applications.

Workflows may include:

1. Quality assessment
2. Alignment
3. Variant calling
4. Variant annotation
5. Comparative analysis
6. Genome visualization

🧬 Whole Exome Sequencing

Analyze exome sequencing data with workflows focused on coding regions and variant identification.

🧬 Targeted Sequencing

Process and analyze targeted sequencing datasets according to the selected genomic regions and research objectives.

🔬 Single-Cell RNA-Seq

Support computational analysis of single-cell sequencing datasets, including:

1. Quality control
2. Cell filtering
3. Normalization
4. Clustering
5. Cell-type analysis
6. Differential expression
7. Dimensionality reduction
8. Visualization

🧪 Variant Analysis

Analyze sequencing datasets to identify and characterize genetic variants.

Possible outputs include:

Variant calling · Annotation · Filtering · Classification · Visualization

Research Applications

Gene Expression Research

Analyze changes in gene expression across experimental conditions.

Genomic Research

Investigate genomic variation and sequencing-derived information.

Transcriptomic Research

Explore transcript-level patterns and molecular pathways.

Comparative Genomics

Compare sequencing datasets between samples, organisms, or experimental groups.

Functional Analysis

Connect sequencing results with biological pathways and functional categories.

Multi-Sample Projects

Process and compare larger sequencing datasets using a consistent analytical workflow.