Chemistry and Apparent Quality of Surface Water and Ground Water Associated with Coal Basins

Personnel of the Arkansas Mining and Mineral Resources Research Institute conducted preliminary investigations on the chemistry and quality of surface and ground water associated with 12 coal-bearing sub-basins in the Arkansas Valley coal field. The coal field is approximately 60 miles long and 33 miles wide but only in 12 areas coal is thick enough and has proper quality to be termed commercial. Both surface and underground sample sites were established in each of the sub-basins with some minor variations in four areas where not all types of sites could be located. Water was collected from 19 surface points and 19 underground points in the established areas. Both field and laboratory analyses were made and elemental contents are reported herein. In the main, the chemistry and water quality suggests that all water is suitable for agricultural and industrial uses. To obtain potable water, treatment must be made to reduce calcium, magnesium, sodium sulfate and iron. The mineral content of the water is due to its contact with coal-bearing zones and, as such, reflects the mineral content of the coal. However, it is recommended that additional studies on the petrography and geochemistry of the coal, overburden and underburden is in order. Also, it is recommended that at least one detailed study be made of one of the coal sub-basins where geologic parameters can be completely established with regard to hydrogeology. This report is an important first step in determining the character and quality of Arkansas coal which must be fully understood to fully utilize this important mineral resource

Dynamics of gene expression and the regulatory inference problem

From the response to external stimuli to cell division and death, the dynamics of living cells is based on the expression of specific genes at specific times. The decision when to express a gene is implemented by the binding and unbinding of transcription factor molecules to regulatory DNA. Here, we construct stochastic models of gene expression dynamics and test them on experimental time-series data of messenger-RNA concentrations. The models are used to infer biophysical parameters of gene transcription, including the statistics of transcription factor-DNA binding and the target genes controlled by a given transcription factor.Comment: revised version to appear in Europhys. Lett., new titl

Relationship between gene co-expression and probe localization on microarray slides

BACKGROUND: Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes. RESULTS: In this work we studied the association between the co-expression of genes, their location on the chromosome and their location on the microarray slides by analyzing a number of eukaryotic expression datasets, derived from the S. cerevisiae, C. elegans, and D. melanogaster. We find that in several different yeast microarray experiments the distribution of the number of gene pairs with correlated expression profiles as a function of chromosomal spacing is peaked at short separations and has two superimposed periodicities. The longer periodicity has a spacing of 22 genes (~42 Kb), and the shorter periodicity is 2 genes (~4 Kb). CONCLUSION: The relative positioning of DNA probes on microarray slides and source plates introduces subtle but significant correlations between pairs of genes. Careful consideration of this spatial artifact is important for analysis of microarray expression data. It is particularly relevant to recent microarray analyses that suggest that co-expressed genes cluster along chromosomes or are spaced by multiples of a fixed number of genes along the chromosome

Integrated analysis of breast cancer cell lines reveals unique signaling pathways

Mapping of sub-networks in the EGFR-MAPK pathway in different breast cancer cell lines reveals that PAK1 may be a marker for sensitivity to MEK inhibitors

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Genotype List String: a grammar for describing HLA and KIR genotyping results in a text string

Knowledge of an individual's human leukocyte antigen (HLA) genotype is essential for modern medical genetics, and is crucial for hematopoietic stem cell and solid-organ transplantation. However, the high levels of polymorphism known for the HLA genes make it difficult to generate an HLA genotype that unambiguously identifies the alleles that are present at a given HLA locus in an individual. For the last 20 years, the histocompatibility and immunogenetics community has recorded this HLA genotyping ambiguity using allele codes developed by the National Marrow Donor Program (NMDP). While these allele codes may have been effective for recording an HLA genotyping result when initially developed, their use today results in increased ambiguity in an HLA genotype, and they are no longer suitable in the era of rapid allele discovery and ultra-high allele polymorphism. Here, we present a text string format capable of fully representing HLA genotyping results. This Genotype List (GL) String format is an extension of a proposed standard for reporting killer-cell immunoglobulin-like receptor (KIR) genotype data that can be applied to any genetic data that use a standard nomenclature for identifying variants. The GL String format uses a hierarchical set of operators to describe the relationships between alleles, lists of possible alleles, phased alleles, genotypes, lists of possible genotypes, and multilocus unphased genotypes, without losing typing information or increasing typing ambiguity. When used in concert with appropriate tools to create, exchange, and parse these strings, we anticipate that GL Strings will replace NMDP allele codes for reporting HLA genotypes

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB

BACKGROUND: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. RESULTS: We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. CONCLUSION: MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML