Analyze and fix .gtf file for Drop-seq pipeline to remove warnings and skipping for genes with issues

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.

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Gist 39d6a8d17d83eb8f17c0926e30809957

This example shows a variant-filtering pipeline for genomic data, using bcftools to process VCF files from two different callers (samtools mpileup and freebayes). The script applies quality and proximity filters, including minimum QUAL scores, distance to indels, and indel cluster thresholds, via the bcftools filter command with options like `-i'%QUAL>10'`, `-g3`, and `-G10`. The output writes filtered VCF files with a LOWQUAL annotation for variants that fail the criteria. The code relies on the bcftools command-line API rather than a visualization library, and the data source is the initial VCF files in the variants directory.

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Plot mean expression of a gene vs other parameters and highlight specific genes using ggrepel

This example uses the scater package to calculate quality metrics for single-cell RNA-seq data, then plots mean gene expression against percent dropout and percent total expression. The ggplot2 code highlights a user-defined set of genes of interest in red, while ggrepel’s `geom_text_repel` labels those genes to avoid overlapping points. Two separate scatterplots are generated from the same `gene_info` data frame, each saved as an SVG, showing how specific genes relate to global expression distributions. The analysis relies on the `newSCESet` and `calculateQCMetrics` functions from scater to derive the plotted metrics.

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Gist 4d45fd0aa5761530e461fa7a84197fe3

This example filters a Perl script that parses a CSV-formatted matrix (`matrice_para.csv`) to extract gene names with no paralogs across 11 species. It writes matching identifiers to a text file (`genes_with_no_para.txt`). The Perl script uses a regular expression to match lines with eleven consecutive zero values, then outputs the first field. The process relies on standard Perl file-handling and pattern-matching operations rather than a visualization library.

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Cell cycle assignment for single cell data using scran and cyclone with gene symbols instead of ensembl IDs

This example demonstrates how to assign cell cycle phases to single-cell RNA sequencing data by converting the pretrained marker gene pairs in the scran package from Ensembl IDs to gene symbols. It shows the resulting phase assignments being added as metadata to a Seurat object for downstream analysis. The code uses the scran cyclone function, the org.Hs.eg.db database for ID conversion via bitr, and dplyr for data manipulation. The visualization implicitly relies on the Seurat object’s structure to store the new cell cycle labels, though no plot is generated in the provided script.

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Translate human to mouse gene symbols for cell cycling genes using biomart and save results for faster reading next time

This example demonstrates a workflow for converting human gene symbols to mouse orthologs for cell cycle genes, saving the results for faster future access. It shows the conversion of two gene lists—S-phase and G2/M-phase markers—from the Seurat `cc.genes` dataset into mouse equivalents. The code uses the `biomaRt` package to query Ensembl databases, specifically calling `useMart` for human and mouse datasets and `getLDS` to map HGNC symbols to MGI symbols. After conversion, the results are written to tab-separated text files for reuse, avoiding repeated live queries.

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Gist 9a3defa4596db38a11fc0ec9549b4834

This example demonstrates a preprocessing workflow for raw paired-end Illumina sequencing data. It shows the trimming of low-quality bases and removal of adapter sequences using the `fastq-mcf` tool from the ea-utils suite, followed by quality control reporting with `FastQC`. The script runs `fastq-mcf` with parameters for a minimum quality score of 20, a mean quality threshold, and a 150-base pair read length, producing trimmed FASTQ files. These outputs are then processed by `FastQC` to generate quality assessment reports for both the forward and reverse reads, illustrating a standard bioinformatics pipeline for high-throughput sequence data preparation.

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Use Seurat to find specific markers of each cell type

This example demonstrates how to identify specific marker genes for each cell type in a Seurat single-cell RNA-seq analysis. It shows a workflow that compares every cell type against all others using the `FindMarkers` function with a ROC test, then filters the results to retain only markers expressed in fewer than 50% of cells in all other populations. The code uses the `tidyverse` and `Seurat` libraries to perform pairwise comparisons, calculate the difference in expression percentages, and summarize the average AUC values. The final output is a curated list of the top 10 markers per cell type, emphasizing those with high discriminatory power.