arXiv subjects

Simon Anders

Publications and source records attributed to Simon Anders.

6 recordsLinked to original sources

arXiv2021

Analysing high-throughput sequencing data in Python with HTSeq 2.0

Summary: HTSeq 2.0 provides a more extensive API including a new representation for sparse genomic data, enhancements in htseq-count to suit single cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes, and Python 3 support. Availability and implementation: HTSeq 2.0 is released as an open-source software under the GNU General Public Licence and available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. Contact: fabio.zanini@unsw.edu.au

q-bio.GN↗

arXiv2014

BADER: Bayesian analysis of differential expression in RNA sequencing data

Identifying differentially expressed genes from RNA sequencing data remains a challenging task because of the considerable uncertainties in parameter estimation and the small sample sizes in typical applications. Here we introduce Bayesian Analysis of Differential Expression in RNA-sequencing data (BADER). Due to our choice of data and prior distributions, full posterior inference for BADER can be carried out efficiently. The method appropriately takes uncertainty in gene variance into account, leading to higher power than existing methods in detecting differentially expressed genes. Moreover, we show that the posterior samples can be naturally integrated into downstream gene set enrichment analyses, with excellent performance in detecting enriched sets. An open-source R package (BADER) that provides a user-friendly interface to a C++ back-end is available on Bioconductor.

stat.AP↗

arXiv2013

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.

q-bio.GN↗

arXiv2007

A variational method based on weighted graph states

In a recent article [Phys. Rev. Lett. 97 (2006), 107206], we have presented a class of states which is suitable as a variational set to find ground states in spin systems of arbitrary spatial dimension and with long-range entanglement. Here, we continue the exposition of our technique, extend from spin 1/2 to higher spins and use the boson Hubbard model as a non-trivial example to demonstrate our scheme.

quant-ph↗

arXiv2006

Quantum communication cost of preparing multipartite entanglement

We study the preparation and distribution of high-fidelity multi-party entangled states via noisy channels and operations. In the particular case of GHZ and cluster states, we study different strategies using bipartite or multipartite purification protocols. The most efficient strategy depends on the target fidelity one wishes to achieve and on the quality of transmission channel and local operations. We show the existence of a crossing point beyond which the strategy making use of the purification of the state as a whole is more efficient than a strategy in which pairs are purified before they are connected to the final state. We also study the efficiency of intermediate strategies, including sequences of purification and connection. We show that a multipartite strategy is to be used if one wishes to achieve high fidelity, whereas a bipartite strategy gives a better yield for low target fidelity.

quant-ph↗

arXiv2006

Fast simulation of stabilizer circuits using a graph state representation

According to the Gottesman-Knill theorem, a class of quantum circuits, namely the so-called stabilizer circuits, can be simulated efficiently on a classical computer. We introduce a new algorithm for this task, which is based on the graph-state formalism. It shows significant improvement in comparison to an existing algorithm, given by Gottesman and Aaronson, in terms of speed and of the number of qubits the simulator can handle. We also present an implementation.

quant-ph↗