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Riege, K.

Publications and source records attributed to Riege, K..

2 recordsLinked to original sources

Bashing irreproducibility with shournal

1The Linux shell is arguably one of the most important computational tools across various scientific disciplines. Its high flexibility makes it the platform of choice for many file operations and smaller scripting tasks. Also, many stand-alone programs are called from the Linux shell - typically followed by multiple command-line parameters. However, in larger analysis projects, keeping track of the work quickly becomes challenging, as a typical shell workflow involves the iterative execution of commands with many parameters, modification of scripts, and editing of configuration files. Too often, researchers find themselves in the uncomfortable position that a computational result generated a few weeks ago can no longer be reproduced, despite having taken great care documenting the work manually. On the other hand, there is a lack of tools able to record the researchers shell activity automatically with reasonably low runtime- and storage overhead. To close this critical gap, we developed shournal, a program that tightly integrates with the Linux shell and automatically records every shell command along with the files it reads or writes. Besides logging command- and file metadata, such as working directory, file path, and checksums, shournal can be configured to archive scripts or configuration files that are not regularly under version control via git or svn.

bioinformatics

Dissecting the DNA binding landscape and gene regulatory network of p63 and p53

The transcription factor (TF) p53 is the best-known tumor suppressor, but its ancient sibling p63 ({Delta}Np63) is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available in recent years, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network (GRN). In particular, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. Here, we use a novel iterative de novo motif search approach in conjunction with extensive publicly available ChIP-seq data to achieve a precise global distinction between p53 and p63 binding sites, recognition motifs, and potential co-factors. We integrate all these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC and, thus, may represent candidates for therapeutic interventions.

genomics