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    The primary intended use case for htseq-count is differential expression analysis, where one compares the expression of the same gene across samples and not the
    The htseq-count output file The program outputs a table with counts for each feature, followed by the special counters, which count reads that were not
    Given a file with aligned sequencing reads and a list of genomic features, a common task is to count how many reads map to each feature. A feature is here an
    Counting ungapped single-end reads¶ ; counts contains a dictionary, which will associate gene IDs with read counts. We use a variant of Python’s usual ; dict type
    These counts are performed using HTSeq2 and are calculated at the gene level. HTSeq-Count files are available in a tab-delimited format with one Ensembl
    Quality Assessment with htseq-qa. Given a FASTQ or SAM file, this script produces a PDF file with plots depicting the base calls and base-call qualities by
    HTSeq is a Python package for analysis of high-throughput sequencing data. For documentation on htseq-count , see htseq-count: counting reads within
    In principle, you could instruct htseq-count to count for each of a gene’s transcript individually, by specifying –idattr transcript_id. However, all reads

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