Frequently asked questions about NGS, bioinformatics and AI-powered analysis.

NGS data analysis is the computational processing and interpretation of next-generation sequencing data. Depending on the experiment, it may include FASTQ quality control, adapter trimming, alignment or pseudoalignment, read quantification, variant calling, methylation analysis, peak calling, taxonomic profiling, statistical analysis, visualization, and biological interpretation.

SciBerg supports common DNA-seq and RNA-seq workflows, including WGS, WES, targeted sequencing, amplicon sequencing, WTS, mRNA-seq, small RNA-seq, single-cell RNA-seq, ChIP-seq, ATAC-seq, bisulfite-seq, MeDIP/DIP-seq, and metagenomics projects.

Yes. Most sequencing providers deliver raw FASTQ files. SciBerg can process these files into project-specific outputs such as QC reports, cleaned reads, alignment files, mapping statistics, count matrices, VCF files, peak tables, methylation tables, taxonomic profiles, figures, and interpretation reports.

At minimum, the analysis team needs the experiment type, organism and reference genome version, sample sheet, library preparation protocol, strandedness for RNA-seq, read layout, sequencing platform, group comparisons, expected outputs, and any special biological or clinical questions.

Yes. Early consultation can help define read depth, replicate number, sequencing mode, library strategy, sample metadata, controls, and downstream analysis endpoints before data are generated.

Large files can usually be exchanged through secure cloud folders, institutional transfer systems, SFTP, Globus, or sequencing-provider download links. The best method depends on file size, data sensitivity, and institutional requirements.

Typical deliverables include QC reports, processed FASTQ files when applicable, BAM or CRAM alignment files, index files, mapping statistics, count tables, VCF files, annotated variants, differential-expression tables, figures, logs, scripts, and a concise analysis report.

Yes. SciBerg workflows can include the exact scripts, command lines, software versions, and parameters used for processing so that the analysis remains transparent and reproducible.

Yes. SciBerg can continue from raw FASTQ files, cleaned FASTQ files, BAM/CRAM files, count matrices, VCF files, or other intermediate results, provided the file format, reference genome, and metadata are clear.

A FASTQ file contains sequencing reads and the corresponding base-quality information. It is often the starting point for NGS data analysis.

Quality control helps identify adapter contamination, low-quality bases, per-base sequence bias, overrepresented sequences, low complexity, GC bias, duplicated reads, unexpected sample composition, and other issues that can influence downstream analysis.

Adapter trimming is required when adapter sequences are present in reads. It is especially important for small RNA-seq, short inserts, amplicon libraries, and low-input libraries, but the need should be evaluated from QC results.

Single-end sequencing reads one end of each DNA fragment. Paired-end sequencing reads both ends, improving mapping, splice-junction detection, structural-variant analysis, transcript reconstruction, and metagenomic classification.

A typical RNA-seq workflow includes FASTQ QC, trimming if needed, alignment or pseudoalignment, gene or transcript quantification, sample-level QC, normalization, exploratory analysis, differential expression, pathway or gene-set analysis, visualization, and reporting.

A count matrix is a table with genes or transcripts as rows and samples as columns. It contains read counts or estimated counts and is commonly used for normalization, clustering, and differential-expression analysis.

Strandedness determines whether reads preserve information about the original RNA strand. Incorrect strandedness settings can reduce assigned reads and distort gene-expression estimates, especially for overlapping genes.

The required number depends on biological variability, study design, effect size, and statistical goals. In many differential-expression studies, at least three biological replicates per group are treated as a practical minimum, but more replicates improve power and robustness.

Yes. Small RNA-seq requires specialised preprocessing because inserts are short and adapter trimming is critical. The workflow may include miRNA annotation, read-length distribution, small-RNA class annotation, differential expression, and visual summaries.

A typical DNA-seq workflow includes FASTQ QC, trimming if needed, alignment to a reference genome, duplicate marking where appropriate, mapping statistics, variant calling, variant filtering, annotation, prioritisation, and reporting.

Whole-genome sequencing covers the entire genome, including coding and non-coding regions. Whole-exome sequencing targets protein-coding exons and selected nearby regions, reducing cost and data volume but missing many non-coding and structural events.

A VCF file is a Variant Call Format file. It stores detected variants, such as single nucleotide variants and small insertions or deletions, together with genomic coordinates, sample genotypes, quality metrics, and annotations.

Yes. Variant annotation may include gene context, predicted consequence, known database identifiers, allele frequencies, functional predictions, clinical relevance where appropriate, inheritance models, and project-specific prioritisation.

Yes. Targeted and amplicon sequencing projects require workflows adapted to the panel design, primer structure, expected coverage, read layout, and variant-frequency range.

Yes. ChIP-seq and ATAC-seq workflows may include QC, trimming, alignment, duplicate handling, peak calling, quality metrics, consensus peak sets, differential accessibility or binding, motif analysis, annotation, and visualization.

Yes. Bisulfite sequencing analysis may include specialised alignment, methylation extraction, cytosine-context analysis, methylation reports, differential methylation, and genomic-region annotation.

Yes. Metagenomic analysis may include host-read removal, taxonomic profiling, functional profiling, assembly, binning, comparative analysis, diversity analysis, and visual reporting, depending on the experimental design.

AI can support literature triage, workflow automation, report drafting, biological interpretation, prioritisation of candidate genes or variants, internal knowledge-base creation, and interactive exploration of results. Scientific validation and transparent documentation remain essential.

Yes. SciBerg can help research and industrial partners design AI-assisted workflows that connect sequencing outputs, biological metadata, curated knowledge, publications, internal reports, and reproducible computational pipelines.

NGS analysis is widely used in clinical genomics, but clinical use requires careful attention to validation, documentation, quality standards, data protection, interpretation rules, and reporting requirements. The exact requirements depend on the intended use and regulatory context.

Yes. SciBerg can support research projects involving clinical or translational samples, provided appropriate data-protection, consent, and project-governance requirements are in place.

Reproducibility is supported by documenting software versions, reference genome versions, parameters, scripts, logs, sample metadata, and workflow decisions. Where appropriate, workflow managers and containerised software environments can also be used.

Yes. SciBerg can prepare clear, publication-oriented figures such as QC summaries, PCA plots, heatmaps, volcano plots, genome-browser tracks, mutation summaries, methylation plots, pathway figures, and custom visualizations.

Yes. Beyond primary processing, SciBerg can help connect results to genes, pathways, molecular mechanisms, disease context, literature, and project hypotheses.

Turnaround depends on data volume, experiment type, number of samples, requested analyses, quality issues, and reporting depth. Small standard projects may be completed relatively quickly, while complex multi-omics or clinical-genomics projects require more time.

Send a short project description, experiment type, number of samples, organism, sequencing platform, data format, target outputs, and deadline through the SciBerg contact page or by email.