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Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis

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TLDR
This work profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry.
Abstract
Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.

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Revealing the vectors of cellular identity with single-cell genomics

TL;DR: Single-cell genomics has now made it possible to create a comprehensive atlas of human cells and has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry.

Seventy-five genetic loci influencing the human red blood cell

Pim van der Harst, +194 more
TL;DR: In this article, the authors carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals and identified 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10−8, which together explain 4-9% of the phenotypic variance per trait.

HSF1 Drives a Transcriptional Program Distinct from Heat Shock to Support Highly Malignant Human Cancers

TL;DR: This work identifies an HSF1-regulated transcriptional program specific to highly malignant cells and distinct from heat shock, active in breast, colon and lung tumors isolated directly from human patients and strongly associated with metastasis and death.

The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia

TL;DR: In this paper, a screen using pooled short hairpin RNAs (shRNAs) identified the ATP-buffering, mitochondrial creatine kinase CKMT1 as necessary for survival of EVI1-expressing cells in subjects with acute myeloid leukemia (AML) and is associated with poor clinical outcome.
Dissertation

The Development of Chemical and Computational Tools to Study Transcriptional Regulation in Cancer

TL;DR: A subset of super enhancers are identified that promote off-target DNA damage from the B cell antibody diversity enzyme AID, leading to double strand break events and translocations in B cell malignancies.
References
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Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Journal ArticleDOI

Model-based Analysis of ChIP-Seq (MACS)

TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
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