SNPredict: A Machine Learning Approach for Detecting Low Frequency Variants in Cancer

Cancer is a genetic disease caused by the accumulation of DNA variants such as single nucleotide changes or insertions/deletions in DNA. DNA variants can cause silencing of tumor suppressor genes or increase the activity of oncogenes. In order to come up with successful therapies for cancer patients, these DNA variants need to be identified accurately. DNA variants can be identified by comparing DNA sequence of tumor tissue to a non-tumor tissue by using Next Generation Sequencing (NGS) technology. But the problem of detecting variants in cancer is hard because many of these variant occurs only in a small subpopulation of the tumor tissue. It becomes a challenge to distinguish these low frequency variants from sequencing errors, which are common in today\u27s NGS methods. Several algorithms have been made and implemented as a tool to identify such variants in cancer. However, it has been previously shown that there is low concordance in the results produced by these tools. Moreover, the number of false positives tend to significantly increase when these tools are faced with low frequency variants. This study presents SNPredict, a single nucleotide polymorphism (SNP) detection pipeline that aims to utilize the results of multiple variant callers to produce a consensus output with higher accuracy than any of the individual tool with the help of machine learning techniques. By extracting features from the consensus output that describe traits associated with an individual variant call, it creates binary classifiers that predict a SNP’s true state and therefore help in distinguishing a sequencing error from a true variant

KLF6 and STAT3 Co-Occupy Regulatory DNA and Functionally Synergize to Promote Axon Growth in CNS Neurons

The failure of axon regeneration in the CNS limits recovery from damage and disease. Members of the KLF family of transcription factors can exert both positive and negative effects on axon regeneration, but the underlying mechanisms are unclear. Here we show that forced expression of KLF6 promotes axon regeneration by corticospinal tract neurons in the injured spinal cord. RNA sequencing identified 454 genes whose expression changed upon forced KLF6 expression in vitro, including sub-networks that were highly enriched for functions relevant to axon extension including cytoskeleton remodeling, lipid synthesis, and bioenergetics. In addition, promoter analysis predicted a functional interaction between KLF6 and a second transcription factor, STAT3, and genome-wide footprinting using ATAC-Seq data confirmed frequent co-occupancy. Co-expression of the two factors yielded a synergistic elevation of neurite growth in vitro. These data clarify the transcriptional control of axon growth and point the way toward novel interventions to promote CNS regeneration

Developmental Chromatin Restriction of Pro‐Growth Gene Networks Acts as an Epigenetic Barrier to Axon Regeneration in Cortical Neurons

Axon regeneration in the central nervous system is prevented in part by a developmental decline in the intrinsic regenerative ability of maturing neurons. This loss of axon growth ability likely reflects widespread changes in gene expression, but the mechanisms that drive this shift remain unclear. Chromatin accessibility has emerged as a key regulatory mechanism in other cellular contexts, raising the possibility that chromatin structure may contribute to the age‐dependent loss of regenerative potential. Here we establish an integrated bioinformatic pipeline that combines analysis of developmentally dynamic gene networks with transcription factor regulation and genome‐wide maps of chromatin accessibility. When applied to the developing cortex, this pipeline detected overall closure of chromatin in sub‐networks of genes associated with axon growth. We next analyzed mature CNS neurons that were supplied with various pro‐regenerative transcription factors. Unlike prior results with SOX11 and KLF7, here we found that neither JUN nor an activated form of STAT3 promoted substantial corticospinal tract regeneration. Correspondingly, chromatin accessibility in JUN or STAT3 target genes was substantially lower than in predicted targets of SOX11 and KLF7. Finally, we used the pipeline to predict pioneer factors that could potentially relieve chromatin constraints at growth‐associated loci. Overall this integrated analysis substantiates the hypothesis that dynamic chromatin accessibility contributes to the developmental decline in axon growth ability and influences the efficacy of pro‐regenerative interventions in the adult, while also pointing toward selected pioneer factors as high‐priority candidates for future combinatorial experiments

Brain-Wide Analysis of the Supraspinal Connectome Reveals Anatomical Correlates to Functional Recovery After Spinal Injury

The supraspinal connectome is essential for normal behavior and homeostasis and consists of numerous sensory, motor, and autonomic projections from brain to spinal cord. Study of supraspinal control and its restoration after damage has focused mostly on a handful of major populations that carry motor commands, with only limited consideration of dozens more that provide autonomic or crucial motor modulation. Here, we assemble an experimental workflow to rapidly profile the entire supraspinal mesoconnectome in adult mice and disseminate the output in a web-based resource. Optimized viral labeling, 3D imaging, and registration to a mouse digital neuroanatomical atlas assigned tens of thousands of supraspinal neurons to 69 identified regions. We demonstrate the ability of this approach to clarify essential points of topographic mapping between spinal levels, measure population-specific sensitivity to spinal injury, and test the relationships between region-specific neuronal sparing and variability in functional recovery. This work will spur progress by broadening understanding of essential but understudied supraspinal populations