Axolomics
This is primarily a site for distribution of axolotl datasets from the Ron Stewart and James Thomson laboratories. These data are available under the Axolomics tab. In addition, this serves as an online resource for large-scale “omic” data on the axolotl (Ambystoma mexicanum). Here you will find data and resources collected for the benefit of the wider axolotl research community, as well as links to important sources of axolotl information. Learn more >
Author: Ron Stewart
MPBind
Aptamers are ‘synthetic antibodies’ that can bind to target molecules with high affinity and specificity. Aptamers are chemically synthesized and their discovery can be performed completely in vitro, rather than relying on in vivo biological processes, making them well-suited for high-throughput discovery. However, a large fraction of the most enriched aptamers in Systematic Evolution of Ligands by EXponential enrichment (SELEX) rounds display poor binding activity. MPBind is a meta-motif-based statistical framework and pipeline to predict the binding potential of SELEX-derived aptamers. Using human embryonic stem cell SELEX-Seq data, MPBind achieved high prediction accuracy for binding potential. Further analysis showed that MPBind is robust to both polymerase chain reaction amplification bias and incomplete sequencing of aptamer pools. These two biases usually confound aptamer analysis. Learn more >
Author: Peng Jiang
OEFinder
OEFinder is a statistical method to identify ordering effect (OE) genes in single-cell RNA-seq data generated by the Fluidigm C1 platform. Specifically, OE genes are the ones showing increased expression in cells captured from sites with small or large plate output IDs. To identify these genes, OEFinder implements an orthogonal polynomial regression along with an one-tailed permutation test. Graphical user interface (GUI) implementations of OEFinder are also available, which allow a user with little computing background to easily identify and characterize OE genes in single-cell RNA-seq data. Learn more >
Author: Ning Leng
Oscope
Oscope is a statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments. Oscope capitalizes on the fact that cells from an unsynchronized population represent distinct states in a system. Oscope utilizes co-regulation information among oscillators to identify groups of putative oscillating genes, and then reconstructs the cyclic order of samples for each group, defined as the order that specifies each sample’s position within one cycle of the oscillation, referred to as a base cycle. The reconstructed order is based on minimizing distance between each gene’s expression and its gene-specific profile defined by the group’s base cycle allowing for phase shifts between different genes. Learn more >
Authors: Ning Leng, Peng Jiang, Christina Kendziorski
SCPattern
SCPattern is an empirical Bayes approach to characterize expression changes in single cell RNA-seq experiments with ordered conditions, such as time points, spacial course, etc. SCPattern identifies genes with expression changes by considering zeros and non-zero cells collectively, and classifies them into directional expression patterns (e.g. Up-Up-Up-Up, Up-Up-Down-Down, etc). SCPattern tests distribution changes of a gene across each pair of adjacent conditions using directional Kolmogorov-Smirnov statistic, and then classify the gene into expression patterns with probability estimates. A graphical user interface of SCPattern is also available. Learn more >
Author: Ning Leng
SinQC
Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for profiling cell-to-cell variability in cell populations. However, the combination of technical noise and intrinsic biological variability makes detecting technical artifacts in scRNA-seq samples particularly challenging. Proper detection of technical artifacts is critical to prevent spurious results during downstream analysis. SinQC (‘Single-cell RNA-seq Quality Control’) is a statistical method and software to detect technical artifacts in scRNA-seq samples by integrating both gene expression patterns and data quality information. Learn more >
Author: Peng Jiang