Gist 6cde24482216f46654efb9e0cef0f166

Gist 3d7c2ec8630fd8451ee2b2ed513c2c4a

This example documents the assembly of a microbial genome from paired-end Illumina sequencing data. It shows the command-line workflow that converts raw FASTQ files to FASTA format and then performs a de novo assembly using the IDBA-UD software. The script demonstrates the use of the `fq2fa` utility to merge and convert paired reads, followed by an `idba_ud` assembly run with a single thread. The final assembly output is moved to a dedicated directory for further analysis, illustrating a typical bioinformatics pipeline for genomic sequence reconstruction.

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Gist babdf0de5f2b986506f621199e6a642d

This Perl script calculates the total length of a genome assembly by reading a FASTA file named `gapCloserMinia.break.broken_assembly.fasta`. It iterates line by line, summing the number of nucleotide characters (A, C, T, G) found on each line, ignoring headers and other non-sequence content. The script uses a simple regex match to detect sequence lines and accumulates the total count, printing the final genome size in base pairs to standard output. The computation is performed entirely with Perl's built-in file handling and string operators, requiring no external libraries or modules.

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Gist fb39aee08b636aec6b8a68d4011241cf

This visualization counts the number of contigs in a genome assembly file. It parses the FASTA-formatted file `gapCloserMinia.break.broken_assembly.fasta` using a Perl script, incrementing a counter each time a line starts with the `>` header character. The final count is printed as a plain integer to the terminal. The script relies solely on Perl's built-in file-handling and regular-expression capabilities, with no external visualization libraries, and does not generate any graphical output.

64% match

Gist 143778d0c50388a317ebc30c0ec5de86

This Perl-based command-line tool calculates GC content and nucleotide percentages from a FASTA file. It reads the input file line by line, skipping headers, and tallies counts of G, C, A, T, and N bases. The output prints GC%, individual base percentages, and the N count, using basic string operations and conditional logic rather than external visualization libraries. The script demonstrates a simple text-based data summary workflow for bioinformatics sequence analysis.

Gist f919aa6a77ff3feb4d8a6d77af5dee85

This example computes the number and length distribution of contiguous sequences from a FASTA file, then writes the results to a tab-delimited text file. It uses Perl’s `Math::Complex` module for mathematical operations, though only basic arithmetic appears. The script reads `gapCloserMinia.break.broken_assembly.fasta`, tracks sequence lengths via regex matching for `[ACTGN]+` lines, and outputs a numbered list of contig sizes, along with the mean and standard deviation. The visualization shows a histogram or table of contig lengths, derived directly from the computed file `tailles_contigues.txt`. There is no graphical rendering; the output is purely textual.

Gist 6344d0fe9ded24d86cd9e580c72e7101

This visualization compares read-length distributions before and after error correction of MinION long reads from Silene latifolia. The code uses the LoRDEC tool to correct and trim reads, then assembly-stats to generate summary statistics, likely producing histograms or cumulative frequency plots. The rendering relies on command-line output from assembly-stats, displaying changes in sequence length and count across three datasets: raw, corrected, and trimmed reads.

Gist ac0e4a9bdb2f03d365c4402d04ecba09

This example shows a command-line workflow for detecting genetic variants from sequencing reads mapped to a reference genome. It first uses `samtools mpileup` to generate a binary call format (BCF) file, then `bcftools call` to produce a VCF file of variants. A second, independent variant call is made using `freebayes` with a ploidy setting of one, outputting its own VCF. The script processes all sorted BAM files in a mapping directory, using the NC_012967.fas reference, demonstrating two standard variant-calling pipelines for haploid samples.

Gist aa8c974207019f98e6d45a49488b2c4b

This visualization shows the process of generating pileup files from sequenced reads for a set of 29 samples. The Bash script iterates through a list of sample identifiers, using the `samtools mpileup` command to produce alignment summaries for each sample against a reference genome. The script relies on the command-line APIs of `samtools` for pileup generation, operating on sorted BAM files. The output from this script would be a text-based pileup format, which is not a graphical visualization but a data-preparation step for subsequent analysis. The code references a reference file and a list of SRR accession numbers, indicating the source is genomic sequencing data.