Reading Alignment and Taxon Identification

Genomics and NGS Data Analysis

1. Reads Alignment and Taxon Assignment

• 0 reads are aligned using BLAST (alignment is performed against non-redundant, nucleotide or environmental databases)
• Each hit is associated with a taxon and each read is assigned to the Lowest Common Ancestor (LCA) of the set of taxa identified

2. Faster Approach for Classification

• Uses marker genes present in nearly all microbes (single copy) or specific to certain clades
• Approach was originally implemented in MetaPhlAn (used to analyze several trillion bases of metagenomic sequences)
• Cannot classify the entire gene content in a sample (requires comparing every read to a known gene)

3. Kraken Approach

• Avoids alignments and uses an exact-match database built from k-mers linked to the LCA of all organisms whose genomes contain that k-mer
• Searches each k-mer in the input read inside the pre-computed database
• Builds a tree of taxa including only the matched LCA
• Assigns a weight to each node equal to the number of k-mers found in the read that are linked to that taxon
• Scores each root-to-leaf (RTL) path in the classification tree and selects the maximal RTL path
• Assigns the read to the taxon corresponding to the leaf of the path

4. Contaminant Removal

• Most RNA-seq libraries select and enrich for mRNAs, but may contain contaminating RNA (rRNAs)
• SortMeRNA software is used to detect and remove contaminating RNA
• Uses a sliding window approach to search for short similarity regions between the reads and the chosen rRNA database
• Selects and removes reads that have enough windows (in a proportion larger than ¼) with all these values being empirical

5. Statistical Robustness

• Coverage value is normally granted by keeping it homogeneously at 100x on average
• In transcriptome analysis, each gene is characterized by different expression levels
• Normalize data using the median k-mer abundance method
• Digital normalization eliminates most of the reads, but loses some connections in the de Bruijn graph and fragments the assembly

6. Assembly Algorithms

• Overlap-Layout-Consensus (OLC) method is used to assemble the original molecule
• Sets up all possible all-against-all pairwise comparisons between the reads
• Builds a graph connecting the partially overlapping reads
• Manipulates the graph to produce a read layout and performs multiple sequence alignments to produce a consensus sequence

7. Trinity Assembly

• Solves the issues of uneven coverage, sequencing biases, and repeat sequences
• Composed of 3 modules: inchworm, chrysalis, and butterfly
• Inchworm phase reconstructs the linear contigs
• Chrysalis phase groups connected components into clusters
• Butterfly phase reconstructs full-length linear transcripts

8. Quality Evaluation

• Assembly statistics include: number of contigs, overall assembly size, median contig length, and N50
• TransRate is a reference-free quality assessment tool for de novo transcriptome assemblies
• Detects multiple common artifacts of assembly, including chimeras, structural errors, incomplete assemblies, and base errors

9. Illumina Sequencing

• Quality control and sequencing: RNA sample quality is assessed using the RNA Integrity Number (RIN) algorithm
• Fragmentation and retrotranscription of the RNA sample
• Hybridization of the cDNA fragments to the flow cell
• Amplification of the sample and addition of labelled nucleotides
• Record of the light signal that comes from the incorporation of specific labelled nucleotides### Big Data in Genomics
• Big data in genomics refers to complex, multi-layered data that:
  • Is obtained by integrating data from different sources
  • Cannot be easily modeled with numerical formulae due to complicated correlations
  • Is dimensionally challenging, requiring significant time and storage
• Example of multi-layered data: Geographic Information System (GIS), which stores, transforms, integrates, and visualizes large amounts of data from different sources related to positions on Earth's surface

Omics Approach and Precise Medicine

• The technological revolution has enabled the study of all genes simultaneously (omics approach) and multiple omic datasets simultaneously (multi-omics approach)
• This has led to the development of precise medicine, which emphasizes the systematic use of individual patient information to select and optimize medical care
• Example: Snyderome, a research study where a researcher measured his own omics (whole genome, transcriptome, proteome, metabolome, and clinical tests) over 14 months to provide a partial overview of his health state and observe possible predispositions to pathologies

Open-Data Agreement and Data Sharing

• The open-data agreement between the scientific community and scientific journals was established due to patent issues arising from the Human Genome Project (HGP)
• The agreement's 2 main principles are:
  • Data must be made freely available to the scientific community
  • Data must be released within 24 hours of generation to encourage research and maximize the benefits of the HGP for society
• The principles have revolutionized the way science is done in life sciences and have become the foundation for all subsequent large international projects

Data Reproducibility and Standards

• Data reproducibility is crucial, and standards and guidelines have been established for submitting data
• Minimum Information About a Microarray Experiment (MIAME) is a set of standards for reporting microarray-based gene expression data
• Reviewers check for compliance with guidelines and ensure data is submitted to public databases before reviewing a paper