Project2BF528.bib

@misc{noauthor_babraham_nodate,
	title = {Babraham {Bioinformatics} - {FastQC} {A} {Quality} {Control} tool for {High} {Throughput} {Sequence} {Data}},
	url = {https://www.bioinformatics.babraham.ac.uk/projects/fastqc/},
	urldate = {2025-09-20},
	file = {Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data:C\:\\Users\\denni\\Zotero\\storage\\EICKKJA6\\fastqc.html:text/html},
}

@article{chandra_type_2022,
	title = {The type 1 diabetes gene {TYK2} regulates β-cell development and its responses to interferon-α},
	volume = {13},
	copyright = {2022 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-022-34069-z},
	doi = {10.1038/s41467-022-34069-z},
	abstract = {Type 1 diabetes (T1D) is an autoimmune disease that results in the destruction of insulin producing pancreatic β-cells. One of the genes associated with T1D is TYK2, which encodes a Janus kinase with critical roles in type-Ι interferon (IFN-Ι) mediated intracellular signalling. To study the role of TYK2 in β-cell development and response to IFNα, we generated TYK2 knockout human iPSCs and directed them into the pancreatic endocrine lineage. Here we show that loss of TYK2 compromises the emergence of endocrine precursors by regulating KRAS expression, while mature stem cell-islets (SC-islets) function is not affected. In the SC-islets, the loss or inhibition of TYK2 prevents IFNα-induced antigen processing and presentation, including MHC Class Ι and Class ΙΙ expression, enhancing their survival against CD8+ T-cell cytotoxicity. These results identify an unsuspected role for TYK2 in β-cell development and support TYK2 inhibition in adult β-cells as a potent therapeutic target to halt T1D progression.},
	language = {en},
	number = {1},
	urldate = {2025-10-20},
	journal = {Nature Communications},
	author = {Chandra, Vikash and Ibrahim, Hazem and Halliez, Clémentine and Prasad, Rashmi B. and Vecchio, Federica and Dwivedi, Om Prakash and Kvist, Jouni and Balboa, Diego and Saarimäki-Vire, Jonna and Montaser, Hossam and Barsby, Tom and Lithovius, Väinö and Artner, Isabella and Gopalakrishnan, Swetha and Groop, Leif and Mallone, Roberto and Eizirik, Decio L. and Otonkoski, Timo},
	month = oct,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cellular immunity, Disease model, Stem-cell differentiation, Type 1 diabetes},
	pages = {6363},
	file = {Full Text PDF:C\:\\Users\\denni\\Zotero\\storage\\XZY5DKJM\\Chandra et al. - 2022 - The type 1 diabetes gene TYK2 regulates β-cell development and its responses to interferon-α.pdf:application/pdf},
}

@article{ewels_multiqc_2016,
	title = {{MultiQC}: summarize analysis results for multiple tools and samples in a single report},
	volume = {32},
	issn = {1367-4811},
	shorttitle = {{MultiQC}},
	doi = {10.1093/bioinformatics/btw354},
	abstract = {MOTIVATION: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis.
RESULTS: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization.
AVAILABILITY AND IMPLEMENTATION: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info
CONTACT: phil.ewels@scilifelab.se.},
	language = {eng},
	number = {19},
	journal = {Bioinformatics (Oxford, England)},
	author = {Ewels, Philip and Magnusson, Måns and Lundin, Sverker and Käller, Max},
	month = oct,
	year = {2016},
	pmid = {27312411},
	pmcid = {PMC5039924},
	keywords = {Computational Biology, High-Throughput Nucleotide Sequencing, Quality Control, Software},
	pages = {3047--3048},
	file = {Full Text:C\:\\Users\\denni\\Zotero\\storage\\Y2M58B26\\Ewels et al. - 2016 - MultiQC summarize analysis results for multiple tools and samples in a single report.pdf:application/pdf},
}

@article{dobin_star_2013,
	title = {{STAR}: ultrafast universal {RNA}-seq aligner},
	volume = {29},
	issn = {1367-4811},
	shorttitle = {{STAR}},
	doi = {10.1093/bioinformatics/bts635},
	abstract = {MOTIVATION: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.
RESULTS: To align our large ({\textgreater}80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of {\textgreater}50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90\% success rate, corroborating the high precision of the STAR mapping strategy.
AVAILABILITY AND IMPLEMENTATION: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.},
	language = {eng},
	number = {1},
	journal = {Bioinformatics (Oxford, England)},
	author = {Dobin, Alexander and Davis, Carrie A. and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R.},
	month = jan,
	year = {2013},
	pmid = {23104886},
	pmcid = {PMC3530905},
	keywords = {Algorithms, Cluster Analysis, Gene Expression Profiling, Genome, Human, Humans, RNA Splicing, Sequence Alignment, Sequence Analysis, RNA, Software},
	pages = {15--21},
	file = {Full Text PDF:C\:\\Users\\denni\\Zotero\\storage\\TVR5HB3H\\Dobin et al. - 2013 - STAR ultrafast universal RNA-seq aligner.pdf:application/pdf},
}

@misc{zhu_verse_2016,
	title = {{VERSE}: a versatile and efficient {RNA}-{Seq} read counting tool},
	copyright = {© 2016, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	shorttitle = {{VERSE}},
	url = {https://www.biorxiv.org/content/10.1101/053306v1},
	doi = {10.1101/053306},
	abstract = {Motivation RNA-Seq is a powerful technology that delivers digital gene expression data. To measure expression strength at the gene level, one popular approach is direct read counting after aligning the reads to a reference genome/transcriptome. HTSeq is one of the most popular ways of counting reads, yet its slow running speed of poses a bottleneck to many RNA-Seq pipelines. Gene level counting programs also lack a robust scheme for quantifying reads that map to non-exonic genomic features, such as intronic and intergenic regions, even though these reads are prevalent in most RNA-Seq data.
Results In this paper we present VERSE, an RNA-Seq read counting tool which builds upon the speed of featureCounts and implements the counting modes of HTSeq. VERSE is more than 30x faster than HTSeq when computing the same gene counts. VERSE also supports a hierarchical assignment scheme, which allows reads to be assigned uniquely and sequentially to different types of features according to user-defined priorities.
Availability VERSE is implemented in C. It is built on top of featureCounts. VERSE is open source and can be downloaded freely from Github (https://github.com/qinzhu/VERSE).
Contact junhyong\{at\}sas.upenn.edu},
	language = {en},
	urldate = {2025-10-24},
	publisher = {bioRxiv},
	author = {Zhu, Qin and Fisher, Stephen A. and Shallcross, Jamie and Kim, Junhyong},
	month = may,
	year = {2016},
	note = {Pages: 053306
Section: New Results},
	file = {Full Text PDF:C\:\\Users\\denni\\Zotero\\storage\\XEH86JNX\\Zhu et al. - 2016 - VERSE a versatile and efficient RNA-Seq read counting tool.pdf:application/pdf},
}

@article{love_moderated_2014,
	title = {Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq2}},
	volume = {15},
	issn = {1474-760X},
	doi = {10.1186/s13059-014-0550-8},
	abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html webcite.},
	language = {eng},
	number = {12},
	journal = {Genome Biology},
	author = {Love, Michael I. and Huber, Wolfgang and Anders, Simon},
	year = {2014},
	pmid = {25516281},
	pmcid = {PMC4302049},
	keywords = {Algorithms, Computational Biology, High-Throughput Nucleotide Sequencing, Models, Genetic, RNA, Sequence Analysis, RNA, Software},
	pages = {550},
	file = {Full Text:C\:\\Users\\denni\\Zotero\\storage\\XJTUIS5R\\Love et al. - 2014 - Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.pdf:application/pdf},
}

@article{subramanian_gene_2005,
	title = {Gene set enrichment analysis: {A} knowledge-based approach for interpreting genome-wide expression profiles},
	volume = {102},
	shorttitle = {Gene set enrichment analysis},
	url = {https://www.pnas.org/doi/10.1073/pnas.0506580102},
	doi = {10.1073/pnas.0506580102},
	abstract = {Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.},
	number = {43},
	urldate = {2025-10-24},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
	month = oct,
	year = {2005},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {15545--15550},
	file = {Full Text PDF:C\:\\Users\\denni\\Zotero\\storage\\B3PN77XU\\Subramanian et al. - 2005 - Gene set enrichment analysis A knowledge-based approach for interpreting genome-wide expression pro.pdf:application/pdf},
}

@article{kuleshov_enrichr_2016,
	title = {Enrichr: a comprehensive gene set enrichment analysis web server 2016 update},
	volume = {44},
	issn = {0305-1048},
	shorttitle = {Enrichr},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC4987924/},
	doi = {10.1093/nar/gkw377},
	abstract = {Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.},
	number = {Web Server issue},
	urldate = {2025-10-24},
	journal = {Nucleic Acids Research},
	author = {Kuleshov, Maxim V. and Jones, Matthew R. and Rouillard, Andrew D. and Fernandez, Nicolas F. and Duan, Qiaonan and Wang, Zichen and Koplev, Simon and Jenkins, Sherry L. and Jagodnik, Kathleen M. and Lachmann, Alexander and McDermott, Michael G. and Monteiro, Caroline D. and Gundersen, Gregory W. and Ma'ayan, Avi},
	month = jul,
	year = {2016},
	pmid = {27141961},
	pmcid = {PMC4987924},
	pages = {W90--W97},
	file = {Full Text PDF:C\:\\Users\\denni\\Zotero\\storage\\WX4QGH36\\Kuleshov et al. - 2016 - Enrichr a comprehensive gene set enrichment analysis web server 2016 update.pdf:application/pdf},
}