Background: Researchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially\nexpressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions\n(DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions,\ntissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of\nDEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that\ncan be streamlined and standardized.\nResults: A-Lister is a lightweight command line and graphical user interface tool written in Python. It can be\nexecuted in a differential expression mode or generic name list mode. In differential expression mode, A-Lister\naccepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff,\nand limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to\nallow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify\nthresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can\nfilter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of\nintersection, fuzzy intersection, difference, and union set operations can also be performed on any number of\npairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single\nA-Lister differential expression command.\nIn generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries\ncomposed of intersection, fuzzy intersection, difference, and union set operations can then be performed across\nthese lists of names.\nConclusions: A-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small\nnumber of most significant entities. These entities can then be further analyzed using visualization, pathway\nanalysis, and other bioinformatics tools.
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