Computational Biology Core - Brown University
Type to start searching
GitHub
Computational Biology Core - Brown University
GitHub
Home
Vignette
Functions
Functions
`adapter_content`: Creates a sorted from most frequent to least frequent abundance table
`calc_adapter_content`: Compute adapter content in reads. This function is only available for macOS/Linux.
`calc_format_score`: Calculate score based on Illumina format
`calc_over_rep_seq`: Calculate sequece counts for each unique sequence and create a table with unique sequences
`dimensions`: Extract the number of columns and rows for a FASTQ file using seqTools.
`find_format`: Gets quality score encoding format from the FASTQ file. Return possibilities are Sanger(/Illumina1.8),
`GC_content`: Calculates GC content percentage for each read in the dataset.
`gc_per_read`: Calculate GC nucleotide sequence content per read of the FASTQ gzipped file
`kmer_count`: Return kmer count per sequence for the length of kmer desired
`overrep_kmer`: Generate overrepresented kmers of length k based on their
None
`per_base_quality`: Compute the mean, median, and percentiles of quality score per base.
`plot_adapter_content`: Creates a bar plot of the top 5 most present adapter sequences.
`plot_GC_content`: Generate mean GC content histogram.
`plot_outliers`: Determine how to plot outliers. Heuristic used is whether their obsexp_ratio differs by more than 1
`plot_overrep_kmer`: Create a box plot of the log2(observed/expected) ratio across the length of the sequence as well as top
None
`plot_per_base_quality`: Generate a boxplot of the per position quality score.
None
`plot_read_length`: Plot a histogram of the number of reads with each read length.
None
`qual_score_per_read`: Calculate the mean quality score per read of the FASTQ gzipped file
`run_all`: Will run all functions in the qckitfastq suite and save the data frames
None
404 - Not found