| Papers, etc. |
 Note on Electronic Access to Journals
The UW Library is generally a paid subscriber to non-open-access journals we cite. You can freely access these
articles from on-campus computers. For off-campus access, follow the "[offcampus]" links below or look at the
library "proxy server" instructions. You will be
prompted for your UW net ID and password.
03/27: -- ---- Organizational Meeting ----
04/03: Monocle 2 -- Xiaojie Qiu (GS)
Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah Pliner, Cole Trapnell,
"Reversed graph embedding resolves complex single-cell developmental trajectories,"
http://biorxiv.org/content/early/2017/02/21/110668
Abstract: Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding
how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories
with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph
embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit
principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to
other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear
trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood
development, and show that loss of function mutations in key lineage transcription factors diverts cells to
alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with
single-cell genomics.
04/10: Game Theory Meets ML -- Scott Lundberg (CSE)
Title: How game theory can help us understand our machine learning models
Abstract: When applying machine learning to computational biology it is important to have accurate models,
and it is also important to understand why they make specific predictions. Unfortunately, these two requirements are
often at odds. Here we demonstrate how to have your cake and eat it too...to have the highest accuracy while
retaining interpretability.
04/17: Genome-Guided Transcriptome Assembly -- Shunfu Mao (EE)
Title: RefShannon: a genome-guided transcriptome assembler using sparse flow decomposition
Abstract: High throughput sequencing of RNA (RNA-Seq) has become a staple in modern molecular biology,
with applications not only in quantifying gene expression but also in isoform-level analysis of the RNA
transcripts. To enable such an isoform-level analysis, a transcriptome assembly algorithm is utilized to stitch
together the observed short reads into the corresponding transcripts. This task is complicated due to the complexity
of alternative splicing - a mechanism by which the same gene may generate multiple distinct RNA transcripts. We
develop a novel genome-guided transcriptome assembler, RefShannon, that exploits the varying abundances of the
different transcripts, in enabling an accurate reconstruction of the transcripts. Our evaluation shows RefShannon
improves sensitivity by 13% to 25% at a given specificity in comparison with state-of-the-art assemblers including
Stringtie and Cufflinks.
04/24: Resolving Multi-copy Duplication in Genomes -- Sudipto Mukherjee (EE)
Abstract:
Structural rearrangement in the DNA has come to the forefront of research in biology due to its association
with diseases. Advancement of sequencing technologies has made it possible to obtain partial information about these
regions. The goal of this project is to design an algorithm for reconstructing a form of structural variation, known
as segmental duplication, by modeling the problem as a discrete matrix completion. By leveraging techniques from
non-convex optimization and structure recovery with missing data, the designed workflow is able to achieve better
result than state-of-the-art algorithms.
05/01: Chromatin Architecture -- Jacob Schreiber (CSE)
Jacob Schreiber, Maxwell Libbrecht, Jeffrey Bilmes, and William Stafford Noble,
"Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture,"
http://biorxiv.org/content/biorxiv/early/2017/01/28/103614.full.pdf
Abstract: Motivation: Recently, Hi-C has been used to probe the 3D chromatin architecture of multiple
organisms and cell types. The resulting collections of pairwise contacts across the genome have connected chromatin
architecture to many cellular phenomena, including replication timing and gene regulation. However, high resolution
(10 kb or finer) contact maps remain scarce due to the expense and time required for collection. A computational
method for predicting pairwise contacts without the need to run a Hi-C experiment would be invaluable in understanding
the role that 3D chromatin architecture plays in genome biology. Results: We describe Rambutan, a deep
convolutional neural network that predicts Hi-C contacts at 1 kb resolution using nucleotide sequence and DNaseI assay
signal as inputs. Specifically, Rambutan identifies locus pairs that engage in high confidence contacts according to
Fit-Hi-C, a previously described method for assigning statistical confidence estimates to Hi-C contacts. We first
demonstrate Rambutanâs performance across chromosomes at 1 kb resolution in the GM12878 cell line. Subsequently, we
measure Rambutanâs performance across six cell types. In this setting, the model achieves an area under the receiver
operating characteristic curve between 0.7662 and 0.8246 and an area under the precision-recall curve between 0.3737
and 0.9008. We further demonstrate that the predicted contacts exhibit expected trends relative to histone
modification ChIP-seq data, replication timing measurements, and annotations of functional elements such as promoters
and enhancers. Finally, we predict Hi-C contacts for 53 human cell types and show that the predictions cluster by
cellular function. Availability: Tutorials and source code for Rambutan are publicly available at
https://github.com/jmschrei/rambutan.
05/08: Large-scale RNA-Seq Analysis -- Daniel Jones (CSE)
05/15: Minor Histocompatibility and Graft-Versus-Host Disease -- David Levine (Biostat)
- PJ Martin, DM Levine, BE Storer, EH Warren, X Zheng, SC Nelson, AG Smith, BK Mortensen, JA Hansen, "Genome-wide minor histocompatibility matching as related to the risk of graft-versus-host disease." Blood, 129, #6 (2017) 791-798.
[offcampus]
Abstract:
The risk of acute graft-versus-host disease (GVHD) is higher after allogeneic hematopoietic cell
transplantation (HCT) from unrelated donors as compared with related donors. This difference has been explained by
increased recipient mismatching for major histocompatibility antigens or minor histocompatibility antigens. In the
current study, we used genome-wide arrays to enumerate single nucleotide polymorphisms (SNPs) that produce
graft-versus-host (GVH) amino acid coding differences between recipients and donors. We then tested the hypothesis
that higher degrees of genome-wide recipient GVH mismatching correlate with higher risks of GVHD after allogeneic
HCT. In HLA-genotypically matched sibling recipients, the average recipient mismatching of coding SNPs was
9.35%. Each 1% increase in genome-wide recipient mismatching was associated with an estimated 20% increase in the
hazard of grades III-IV GVHD (hazard ratio [HR], 1.20; 95% confidence interval [CI], 1.05-1.37; P = .007) and an
estimated 22% increase in the hazard of stage 2-4 acute gut GVHD (HR, 1.22; 95% CI, 1.02-1.45; P = .03). In HLA-A,
B, C, DRB1, DQA1, DQB1, DPA1, DPB1-phenotypically matched unrelated recipients, the average recipient mismatching of
coding SNPs was 17.3%. The estimated risks of GVHD-related outcomes in HLA-phenotypically matched unrelated
recipients were low, relative to the large difference in genome-wide mismatching between the 2 groups. In contrast,
the risks of GVHD-related outcomes were higher in HLA-DP GVH-mismatched unrelated recipients than in HLA-matched
sibling recipients. Taken together, these results suggest that the increased GVHD risk after unrelated HCT is
predominantly an effect of HLA-mismatching.
05/22: Tentative: a framework for single-cell RNA-seq learning -- Sumit Mukherjee (EE)
05/29: -- Holiday
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