2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

/pdf/a-complete-bibliography-of-publications-in-sigcse-bulletin-1aolnsl5m4.pdf

A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

Discoveries of exons that correctly code for a gene are crucial in predicting protein sequences and determining protein function. This can be accomplished using one of two methods: through computational tools or through experimental laboratory techniques. Regardless of how these exon coding regions are derived, they need to be properly annotated before they are made available to the scientific community [1]. Large genomic data repositories (such as NCBI’s Genbank [2]) have their own standards of annotating and searching through their data. Along with information pertaining to genes, the annotation includes information ranging from reference data, conditions involved with experimentation, as well as other cumbersome metadata. Exons can be relatively small compared to whole genomes, so precise querying and retrieval becomes a very difficult task. Metadata and redundancy stifles the querying process as well as the development of new hypotheses by returning to the user unwanted results [3].

Automated gene processing and exon sequence retrieval

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Automated gene processing and exon sequence retrieval