Practical Bioinformatics I

Explain sanger sequencing as a method validating next generation data, sequencing genes or plasmids, and show how dideoxynucleotides terminate synthesis with chromatograms from gel electrophoresis in abi and scf formats.

Evaluate the chromatogram as the initial step to identify sequencing problems by analyzing the signal-to-noise ratio, aiming for 150–1600, and noting issues like poor template quality or contamination.

Visualize multiple chromatogram tracks in Teal, arrange four images in GIMP with precise height and offsets, and export the final visuals highlighting SNPs and allelic variation.

Learn to edit files in Mega, save chromatograms as scf, and use alignment, blast, and translation tools to analyze sequences. Export sequences as fasta, and mask upstream and downstream regions.

Install finch tv, open ab1 or scf trace files, and explore sequences with vertical and horizontal scales; export to fasta or fastq for analysis and prepare segments for blast.

Explore practical sequencing platforms: Illumina's cyclic reversible termination with bridge amplification and fluorescence detection, and Ion Torrent's emulsion-pcr with pH-based signal, highlighting error profiles and cost evolution.

Explore single-molecule sequencing on pacific biosciences and nanopore platforms, detailing smrt sequencing with hairpin adapters in zero-mode waveguides and motor-protein driven nanopore sensing with rnn-based base calling.

Explore how high-throughput sequencing enables genome and epigenomics studies, including SNPs, indels, and structural variants. Investigate regulatory elements, chromatin accessibility, and three-dimensional genome architecture with hi-c and chia-pet.

Explores how high-throughput sequencing drives transcriptomics, metagenomics, and cancer research, detailing rna-seq methods, 16s and shotgun metagenomics, exome and genome sequencing, tcga/icgc, and single-cell analyses.

Explore high-throughput sequencing methods with the sequencing method explorer, organized into transcriptome, genome, and epigenome, including dnase-seq, chia-pet, and hi-c with introductions, advantages and disadvantages, and references.

Explore how illumina and nanopore data become fastq files—covering raw signals in bcl and fast5, header and quality score encoding (ascii, qphred), and fastqc-based checks across sanger, solexa, and illumina.

Explore FASTA and GenBank formats, including unique identifiers, and extensions like fna, faa, and frn, plus locus information, definitions, and accessions.

Understand gff3 and gtf annotation formats, their nine-column structure, and key fields such as seqid, source, type, start, end, score, strand, phase, and attributes; also learn bed track basics.

Explore sam and bam files, the text and binary formats for sequence alignments to a reference. Identify key fields like qname, flag, rname, pos, mapq, cigar, chimera reads, and tags.

Understand vcf and bcf formats and the vital vcf header, and explore annotation fields: chromosome, position, id, ref, alt, qual, filter, plus sample fields like gt, gq, dp, and af.

Learn to extract regions from a sequence using extractseq by specifying start and end, handle fasta and genbank formats, and manage overlap removal with blastn and output options.

Apply the range extractor dna to extract subsequences from fasta sequences, using start, end, length, center, reverse complement, and support for multiple ranges within the same sequence.

Learn how to use the window extractor dna to extract up and down regions from aligned dna sequences, including promoter and gene regions, using Clustal Omega and fasta outputs.

Learn how to extract chromosome sequences from a genome by parsing genbank files to pull features such as genes and mrna, using fasta .ffn files, with examples from Saccharomyces cerevisiae.

Learn to extract gene, mRNA, and CDS from a genome using GenBank feature filters, qualifiers, and tags; perform reverse-complement extractions, and analyze upstream and downstream regions.

Explore advanced options to extract genomic features, including combining the cdss of a single gene into one sequence, translating cds to protein, and exporting in GenBank and NCBI FASTA formats.

Extract cdss, mrnas, and translations from GenBank feature tables using coderet and orfer, and generate a summary table with record name, translations, noncoding regions, mrnas, and cdss.

Align sequencing reads to transgenic vectors using blastn, convert fastq to fasta, extract reads with seqtk_subseq, and build a Galaxy database to identify insertion sites.

Extract sequences with seqtk_subseq from fasta or fastq id lists. Produce tab-separated ids and sequences, and adjust bed-based extractions by subtracting one nucleotide before re-uploading to Galaxy.

Extract marker regions from a sequence alignment to study phylogenetic relationships. Blastn against ngs reads, align with clustalw and clustal omega, then extractalign to create a fasta alignment.

Learn to extract features from gff and gtf files using Galaxy, focusing on genes, exons, and chromosomes, with Ensembl data and practical file handling.

Extract sequences from a reference genome for bed/gff/vcf intervals using bedtools getfasta. Learn to control headers, strandness, and outputs including tab-separated reports, with reverse complement handling for negative strands.

Use bedtools flank to create two nucleotides upstream of 5' ends for intervals in bed/gff/vcf files, with strand-aware -l and -r settings and optional getfasta conversion.

Use bedtools complement to identify intergenic regions and untranslated regions, extract introns and exons, and merge bed files while sorting by chromosome and start position, with closest-feature analysis.

Learn to retrieve transcriptome fastq files, assess quality with fastqc, remove adapters with cutadapt, and map reads to a reference genome using bowtie2, producing mapped fastq reads.

this lecture shows filtering bam files to keep mapped reads by removing unmapped reads with -q and -f flags, sorting by coordinate, and optionally converting to fastq.

Use bcftools consensus to build a consensus sequence from vcf variants on a reference fasta; call variants with freebayes, filter by read-depth and quality, then normalize and extract the consensus.

Learn to normalize called variants, left-align and normalize indels, and extract consensus sequences, using iupac codes and haplotypes to compare vcf and fasta data.

Extract fields from vcf or bcf files with bcftools query, producing a tab-delimited, user-defined output. The lecture introduces the lyve-set phylogenomics pipeline for studying foodborne pathogens at the genomics level.

Explore a pipeline to improve yeast genome assembly and detect chromosomal translocations. Convert ab1 files to fastq and assess quality with fastqc.

Learn to extract pairwise information from alignments with aligncopypair, capturing identity, similarity, length, and gaps using mafft and clustal omega, plus format conversions to fasta.

Convert fasta to tabular and back, adjust headers, and extract ids with configurable columns in galaxy for streamlined downstream analyses such as multiple sequence alignment and phylogenetic trees.

Explore converting bam to bed with bedtools, generating bed, bed12, and bedpe outputs for low-coverage sequencing in humans and wood mice, including split reads and mapping quality.

Convert BAM to FASTQ by extracting unmapped reads and merging them into forward and reverse FASTQ files. Use r2 and q2 options to output read2 separately.

Split sequence(s) into smaller sequences using seqretsplit, converting multifasta files into individual fasta records and configuring options such as features, first-record output, and output formats.

Split fasta into fixed-size sequences or codon-sized blocks, extract coding sequences with cds and codons using extractfeat, and compare codon-level differences with alignment concepts such as rat versus human examples.

Learn to split fasta files in Galaxy with split fasta, creating separate records or parts. Compare to seqretsplit, handle inputs like GenBank, then download outputs with ids as filenames.

Learn to split sequences at long n strings with seqtk_cutn, remove gaps by adjusting nonn penalties, and extract the removed region in de bruijn graph-based genome assembly workflows using simplitigs.

Learn to concatenate multiple fasta exons into a single sequence using union and combine fasta, inspect overlaps, and verify the resulting length (444) and gc content with infoseq.

Learn to merge two fasta/q files with seqtk_mergefa, using masking with maskseq and the take intersection option to reveal differences, heterozygous sites, and downstream comparison with clustal omega.

Apply bedtools mergebed to merge overlapping and nearby intervals, testing distance thresholds from ten to twenty bases while noting enhancer domains that inform gene pathogenicity.

Learn how to merge multiple sorted alignment files with samtools merge, manage headers, and perform region-based merges while mapping fastq reads to a reference using bowtie2 in Galaxy.

Learn how to merge vcf/bcf files with bcftools concat, control regions with restrict to or bed files, remove duplicates, and output unified vcf results.

Learn how to remove poly-a tails from nucleotide sequences with trimest, including minimum tail length, allowed mismatches, and optional reverse-complement conversion for poly-t tails; see output headers reflect removals.

Remove redundant sequences using the scab redundant program, applying 95% similarity threshold (and 99%), align the human HP messenger RNA against the non-redundant nucleotide database, and assess results with BLAST.

Learn to remove adapter sequences from nucleotide reads with vector strip, adjusting mismatch tolerance and interpreting the output statistics that report removed regions.

Use cutadapt to remove adapters from fastq and fasta files, trim low-quality bases, discard reads shorter than forty bases, and apply a two-stage workflow for anchored, non-internal, and regular adapters.

Remove adapters and low-quality bases from FASTA/FASTQ reads by applying a 20 quality cutoff, trimming 3' and 5' ends, and using wild cards and mismatches options.

Shows how to subtract overlapping intervals from bed/gff/vcf files, using a reference genome and overlap-based removals, with examples in galaxy.