Current Challenges in Metagenomics: an Overview Chandan Pal 17 th December, GoBiG Meeting

Why Metagenomics? Study of microbial communities Unculturable or difficult to grow in lab Understanding microbial population, diversity

Metagenomic Workflow Environmental Sampling Library preparation Sequencing (NGS platforms) Quality controlling of raw reads (duplicated reads, low quality reads) Read alignment to reference databases Assembling the reads into longer contigs Predicting genes and assigning function to the genes

Challenges Sampling  bioinformatics analysis Sampling (biodiversity richness, evenness, Extreme environments, DNA extraction) Analysing replicates (soil samples, water samples etc.) Identification of Microbial communities - Based on knowledge of similar organisms - Variability among closely related species (e.g. E.coli K12 vs 0157:H7)

Challenges Large number of sequencing data, data storage, processing, lack of supercomputing resources, no comprehensive pipeline Sequence artifacts (sequence clustering: CD-Hit, SEED) (errors: SLP, pyronoise, Ampliconnoise)) Metagenomic Assembly (gene rearrangements) - highly conserved sequences from different species - assembling all reads into single sequence - lateral gene transfer - low coverage - Speed (GPU, Cloud computing) Gene prediction (frameshits at low coverage) & Functional annotation (homology based, depends on Public database)

Challenges Quality and Context of metadata (meaningful comparison between studies) Integrative approach Working on the Server or cloud computing Data submission (one Illumina run= 600 Gb = 4-5 times fastq raw files) Reverse metagenomics approach Biotechnological applications