Patrick Flaherty

Bio: Patrick Flaherty is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Inference & Cluster analysis. The author has an hindex of 15, co-authored 43 publications receiving 5879 citations. Previous affiliations of Patrick Flaherty include Lawrence Berkeley National Laboratory & VA Palo Alto Healthcare System.

Functional profiling of the Saccharomyces cerevisiae genome.

Abstract: Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast

Abstract: We demonstrate the efficacy of a genome-wide protocol in yeast that allows the identification of those gene products that functionally interact with small molecules and result in the inhibition of cellular proliferation. Here we present results from screening 10 diverse compounds in 80 genome-wide experiments against the complete collection of heterozygous yeast deletion strains. These compounds include anticancer and antifungal agents, statins, alverine citrate, and dyclonine. In several cases, we identified previously known interactions; furthermore, in each case, our analysis revealed novel cellular interactions, even when the relationship between a compound and its cellular target had been well established. In addition, we identified a chemical core structure shared among three therapeutically distinct compounds that inhibit the ERG24 heterozygous deletion strain, demonstrating that cells may respond similarly to compounds of related structure. The ability to identify on-and-off target effects in vivo is fundamental to understanding the cellular response to small-molecule perturbants.

Oncogenic BRAF Mutation with CDKN2A Inactivation Is Characteristic of a Subset of Pediatric Malignant Astrocytomas

Abstract: Malignant astrocytomas are a deadly solid tumor in children. Limited understanding of their underlying genetic basis has contributed to modest progress in developing more effective therapies. In an effort to identify such alterations, we performed a genome-wide search for DNA copy number aberrations (CNA) in a panel of 33 tumors encompassing grade 1 through grade 4 tumors. Genomic amplifications of 10-fold or greater were restricted to grade 3 and 4 astrocytomas and included the MDM4 (1q32), PDGFRA (4q12), MET (7q21), CMYC (8q24), PVT1 (8q24), WNT5B (12p13), and IGF1R (15q26) genes. Homozygous deletions of CDKN2A (9p21), PTEN (10q26), and TP53 (17p3.1) were evident among grade 2 to 4 tumors. BRAF gene rearrangements that were indicated in three tumors prompted the discovery of KIAA1549-BRAF fusion transcripts expressed in 10 of 10 grade 1 astrocytomas and in none of the grade 2 to 4 tumors. In contrast, an oncogenic missense BRAF mutation (BRAF(V600E)) was detected in 7 of 31 grade 2 to 4 tumors but in none of the grade 1 tumors. BRAF(V600E) mutation seems to define a subset of malignant astrocytomas in children, in which there is frequent concomitant homozygous deletion of CDKN2A (five of seven cases). Taken together, these findings highlight BRAF as a frequent mutation target in pediatric astrocytomas, with distinct types of BRAF alteration occurring in grade 1 versus grade 2 to 4 tumors.

Phase II Study of Gemcitabine, Carboplatin, and Iniparib As Neoadjuvant Therapy for Triple-Negative and BRCA1/2 Mutation–Associated Breast Cancer With Assessment of a Tumor-Based Measure of Genomic Instability: PrECOG 0105

Abstract: Purpose This study was designed to assess efficacy, safety, and predictors of response to iniparib in combination with gemcitabine and carboplatin in early-stage triple-negative and BRCA1/2 mutation–associated breast cancer. Patients and Methods This single-arm phase II study enrolled patients with stage I to IIIA (T ≥ 1 cm) estrogen receptor–negative (≤ 5%), progesterone receptor–negative (≤ 5%), and human epidermal growth factor receptor 2–negative or BRCA1/2 mutation–associated breast cancer. Neoadjuvant gemcitabine (1,000 mg/m2 intravenously [IV] on days 1 and 8), carboplatin (area under curve of 2 IV on days 1 and 8), and iniparib (5.6 mg/kg IV on days 1, 4, 8, and 11) were administered every 21 days for four cycles, until the protocol was amended to six cycles. The primary end point was pathologic complete response (no invasive carcinoma in breast or axilla). All patients underwent comprehensive BRCA1/2 genotyping, and homologous recombination deficiency was assessed by loss of heterozygosity (HRD-L...

Genome-Wide Requirements for Resistance to Functionally Distinct DNA-Damaging Agents

Abstract: The mechanistic and therapeutic differences in the cellular response to DNA-damaging compounds are not completely understood, despite intense study. To expand our knowledge of DNA damage, we assayed the effects of 12 closely related DNA-damaging agents on the complete pool of ~4,700 barcoded homozygous deletion strains of Saccharomyces cerevisiae. In our protocol, deletion strains are pooled together and grown competitively in the presence of compound. Relative strain sensitivity is determined by hybridization of PCR-amplified barcodes to an oligonucleotide array carrying the barcode complements. These screens identified genes in well-characterized DNA-damage-response pathways as well as genes whose role in the DNA-damage response had not been previously established. High-throughput individual growth analysis was used to independently confirm microarray results. Each compound produced a unique genome-wide profile. Analysis of these data allowed us to determine the relative importance of DNA-repair modules for resistance to each of the 12 profiled compounds. Clustering the data for 12 distinct compounds uncovered both known and novel functional interactions that comprise the DNA-damage response and allowed us to define the genetic determinants required for repair of interstrand cross-links. Further genetic analysis allowed determination of epistasis for one of these functional groups.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection.

Abstract: We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).

Protein Misfolding, Functional Amyloid, and Human Disease

Abstract: Peptides or proteins convert under some conditions from their soluble forms into highly ordered fibrillar aggregates. Such transitions can give rise to pathological conditions ranging from neurodegenerative disorders to systemic amyloidoses. In this review, we identify the diseases known to be associated with formation of fibrillar aggregates and the specific peptides and proteins involved in each case. We describe, in addition, that living organisms can take advantage of the inherent ability of proteins to form such structures to generate novel and diverse biological functions. We review recent advances toward the elucidation of the structures of amyloid fibrils and the mechanisms of their formation at a molecular level. Finally, we discuss the relative importance of the common main-chain and side-chain interactions in determining the propensities of proteins to aggregate and describe some of the evidence that the oligomeric fibril precursors are the primary origins of pathological behavior.

Active Learning Literature Survey

Abstract: The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant or easily obtained, but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for successful active learning, a summary of problem setting variants and practical issues, and a discussion of related topics in machine learning research are also presented.