12 research outputs found
Syncopy - Systems Neuroscience Computing in Python
Syncopy (www.syncopy.org) is aimed to be a completely open source, user-friendly yet powerful data analysis suite for the Neurosciences. It is developed in Python and makes extensive use of distributed computing via Dask, and achieves low memory footprints by using on-disc hdf5 data structures in the backend per default. For our users, we supply highly abstracted frontend functions, which allow using the same analysis code irrespective of whether the code is run on their local machines or on an HPC cluster. We aim to interface with existing data formats (e.g. Neurodata Without Borders, NWB) and community tools (e.g. Fieldtrip), and foster reproducibility by creating and preserving a lot of meta-information during processing
De-Novo Discovery of Differentially Abundant Transcription Factor Binding Sites Including Their Positional Preference
Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open-source Java framework Jstacs and as a stand-alone application at http://www.jstacs.de/index.php/Dispom
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An integrated omics analysis reveals molecular mechanisms that are associated with differences in seed oil content between Glycine max and Brassica napus
Abstract
Background: Rapeseed (Brassica napus L.) and soybean (Glycine max L.) seeds are rich in both protein and oil, which
are major sources of biofuels and nutrition. Although the difference in seed oil content between soybean (~ 20%) and
rapeseed (~ 40%) exists, little is known about its underlying molecular mechanism.
Results: An integrated omics analysis was performed in soybean, rapeseed, Arabidopsis (Arabidopsis thaliana L. Heynh),
and sesame (Sesamum indicum L.), based on Arabidopsis acyl-lipid metabolism- and carbon metabolism-related genes.
As a result, candidate genes and their transcription factors and microRNAs, along with phylogenetic analysis and
co-expression network analysis of the PEPC gene family, were found to be largely associated with the difference
between the two species. First, three soybean genes (Glyma.13G148600, Glyma.13G207900 and Glyma.12G122900)
co-expressed with GmPEPC1 are specifically enriched during seed storage protein accumulation stages, while the
expression of BnPEPC1 is putatively inhibited by bna-miR169, and two genes BnSTKA and BnCKII are co-expressed
with BnPEPC1 and are specifically associated with plant circadian rhythm, which are related to seed oil biosynthesis. Then,
in de novo fatty acid synthesis there are rapeseed-specific genes encoding subunits β-CT (BnaC05g37990D) and BCCP1
(BnaA03g06000D) of heterogeneous ACCase, which could interfere with synthesis rate, and β-CT is positively regulated by
four transcription factors (BnaA01g37250D, BnaA02g26190D, BnaC01g01040D and BnaC07g21470D). In triglyceride synthesis,
GmLPAAT2 is putatively inhibited by three miRNAs (gma-miR171, gma-miR1516 and gma-miR5775). Finally, in rapeseed
there was evidence for the expansion of gene families, CALO, OBO and STERO, related to lipid storage, and
the contraction of gene families, LOX, LAH and HSI2, related to oil degradation.
Conclusions: The molecular mechanisms associated with differences in seed oil content provide the basis for
future breeding efforts to improve seed oil content