Metagenomic pipeline determines the composition and abundance of organisms, offering a snapshot of diversity within the sample. Additionally, it identifies marker genes, functional proteins, and cellular pathways in the data. Our pipeline’s results facilitate inference of plausible interactions among organisms and their environment. They can lead to the discovery of putative roles of organisms and communities in biological and environmental processes.
Firstly, it lays the groundwork by De-Noising the input sample. The tools remove interfering contaminants to ensure accurate downstream analysis.
Then, it performs Taxonomic Classification to identify taxa present in the data and Abundance Profile to quantify the relative abundance of determined taxa. The selection of tools for profiling data depends on the features of your input sample and research objectives.
As the next step, Binning groups contigs that belong to individual organism and places them into a cluster known as ‘bin’. This process is used for recovering nearly complete contigs in heterogeneous samples to reconstruct metagenome-assembled genomes (MAGs) and identify novel species; thus making the most out of your sample data.
Lastly, Functional Annotation involves identifying coding regions and highlighting relevant genes, translated proteins, and notable pathways in the sample data. This step brings the analysis to fruition, offering detailed outcomes and potential applications of your research.
Our state-of-the-art workflow ensures that your samples are examined using the latest credible software and tools. We deliver spot-on reports that will help you interpret the results and convey the rationale behind your research effectively.
Labs sequence metagenomes from various sources using NGS platforms. The sequenced data is stored in .fastq.gz files, which serve as input for the execution of the Metagenomic pipeline. The tools conduct a quality check to spot any discrepancies before starting the analysis. Read files are assessed to detect contamination and outline characteristics such as quality, coverage, length, and GC content.
Short reads, leftover adapter sequences, repeats, or low-quality scores in reads lead to noise in the data. Therefore, we incorporate robust tools to remove these artifacts to streamline downstream process.
Metagenomic samples collected from sources such as river water, agricultural soil, and clinical specimens often contain host-derived impurities. These impurities can result in incorrect assessments of the target microbiome’s diversity and abundance. The tools remove host contamination to enhance the sensitivity of analysis; hence facilitating the accurate detection of rare microbial species.
During library preparation, processes like PCR amplification and DNA/RNA fragmentation can lead to over or under-representation of specific fragments in the sequence. This gives rise to duplicate reads, causing inflated abundance estimates and false negatives in small samples. Filtering out duplicates resolves these issues and boosts the accuracy of subsequent analysis.
This step concludes the preparation of input data for analysis. Refined reads are merged, sorted, and converted to a single .fastq file. The De Bruijn graph method is applied to the .fastq file to generate a de novo contig assembly. This assembly represents all the reads in your data.
Metagenomic samples can contain a wide variety of microorganisms. The pipeline incorporates curated profiling tools that compare contig assembly against an extensive database to determine known microbial sequences in the data. The results display taxonomic groups assigned to contigs along with their relative abundance in the sample.
The pipeline generates a .bam file containing detailed read alignment data by aligning filtered reads from the sample to either contig assembly or a reference database of known genes/genomes.
The .bam file is used as input to determine contig depth. Contigs from the same organism display consistent values in genomic features such as contig depth, sequence composition, and coverage patterns. The tool uses this fact as the differentiating criterion for segregating contigs into separate bins. Subsequent assessment of the ‘genomic bins’ generates a detailed report on the binning metrics.
Scalable tools examine contig assembly to locate coding regions. The results display coding genes and translated protein sequences along with corresponding statistical information. Enrichment analysis reveals significant biological pathways and processes represented in the data.
