bluuebunny/crossref_metadata_2025_split huggingface dataset:
Studies of yeast Kluyveromyces lactis mutations conferring super‐secretion of recombinant proteins
Authors: Bartkevičiute Danguole , Sasnauskas Kestutis 
Published: December 2002

AbstractWe have isolated mutants responsible for a super‐secretion phenotype in Kluyveromyces lactis using the gene coding for a Bacillus amyloliquefaciens α‐amylase as a marker for secretion. These mutations defined two groups, dominant and recessive. The recessive mutant strain, which secreted the heterologous protein in five‐fold excess compared to the wild‐type strain, was used for the cloning of genes, restraining the super‐secreting phenotype. In screening for genes affecting super‐secreting phenotype, we found that multiple copies of 10 different independently isolated DNA sequences suppressed the super‐secreting phenotype. The first among the genes characterized, named KlSEL1 (‘secretion lowering’) showed homology to Saccharomyces cerevisiae ORF YML013w. The KlSEL1 gene is predicted to encode a polypeptide of 620 amino acid residues containing a putative transmembrane domain and UBX domain, characteristic for the ubiquitin‐regulatory proteins. We demonstrated that the disruption of the SEL1 orthologues in K. lactis and S. cerevisiae conferred the super‐secreting phenotype. SEL1 isolated from S. cerevisiae suppressed the super‐secretion phenotype in K. lactis klsel1 strain, likewise homologous KlSEL1. No other phenotypic features for strains lacking the SEL1 gene were noticed except for the S. cerevisiae mutant growth being notably slower than in a wt strain. No growth changes were observed in the K. lactis klsel1 mutant. The set of genes (suppressors of over‐secreting phenotype) could be attractive for further analysis of gene functions, super‐secreting mechanisms and construction of new strains. This collection could be useful for the expedient construction of reduced yeast genomes, optimized for heterologous protein secretion. The KlSEL1 gene has been assigned EMBL Accession No. AJ488285. Copyright © 2002 John Wiley & Sons, Ltd.
The acetyl co‐enzyme A synthetase genes of Kluyveromyces lactis
Authors: Zeeman A.‐M. , Steensma H. Y. 
Published: December 2002

AbstractTwo Kluyveromyces lactis genes encoding acetyl co‐enzyme A synthetase isoenzymes were isolated. One we named KlACS1, as it has high similarity to the ACS1 gene of Saccharomyces cerevisiae. The other gene, KlACS2, showed more similarity to S. cerevisiae ACS2 than to KlACS1 or ScACS1. This suggests that divergence of the two isogenes occurred before the evolutionary separation of the species and that the different functions have been conserved. In line with this idea is the regulation of transcription of the genes. The mode of regulation appeared to be maintained between ScACS1 and KlACS1 and between ScACS2 and KlACS2. The KlACS1 transcript was absent in glucose‐grown cells, whereas transcription levels in ethanol‐ and acetate‐grown cells were high. Disruption of the KlACS1 gene did not result in growth defects on glucose or ethanol. The growth rate on acetate, however, was reduced by a factor of two. KlACS2 was expressed at similar levels during growth on glucose and acetate, whereas expression on ethanol was slightly higher. A null mutant in this gene showed a reduced growth rate on all three carbon sources. Taken together, these data suggest that KlACS2 is used during growth on glucose and that KlACS1 is most dominant during growth on acetate. Strains in which both ACS genes are deleted could only be retrieved when a plasmid containing the ACS2 gene was present, suggesting that the double mutant is lethal. Tetrad analysis confirmed that non‐viable spores with a deduced Klacs1Klacs2 genotype germinated but could not divide further. It therefore appears that, as in S. cerevisiae, the pyruvate dehydrogenase bypass formed by the enzymes pyruvate decarboxylase, acetaldehyde dehydrogenase and acetyl co‐enzyme A synthetase is essential for growth. These results are in apparent contradiction with the growth on glucose of a strain with a disruption in the only structural pyruvate decarboxylase gene of K. lactis. Residual enzyme activity might, however, account for this discrepancy, or Acs fulfils an additional as yet unknown function, separate from its enzymatic activity. The sequences of KlACS1 and KlACS2 have been deposited in the EMBL database under Accession Nos AF061265 and AF134491, respectively. Copyright © 2002 John Wiley & Sons, Ltd.
Relèvements des algèbres lisses et de leurs morphismes
Authors: Arabia A. 
Published: December 2002

Let

R

be a commutative ring and let

I

be any ideal of

R

, put

{\bf\it\bar R} : = {\bf\it R} / {\bf\it I}

. We prove that for any smooth

\bf\it\bar R

-algebra

\bf\it\bar A

there exist a smooth

R

-algebra

A

such that

\bf\it\bar A

is isomorphic to

{\bf\it\bar R} \otimes_{\bf\it R} {\bf\it A}

. We also show that for any morphism of smooth

\bf\it\bar R

-algebras

{\it\bar a} : {\bf\it\bar A} \rightarrow {\bf\it\bar B}

, there exist a morphism of smooth

R

-algebras

{\it a} : {\bf\it A} \rightarrow {\bf\it B}

such that

{\bf 1} \otimes {\it a} : {\bf\it\bar R} \otimes {\bf\it A} \rightarrow {\bf\it\bar R} \otimes {\bf\it B}

is isomorphic to

{\it\bar a} : {\bf\it\bar A} \rightarrow {\bf\it\bar B}

. As a corollary, when

R

is noetherian, we show that for any smooth

\bf\it\bar R

-algebra

\bf\it\bar A

there exist a very smooth weakly complete algebra

{\bf\it A}^\dagger

over

R

such that

{\bf\it\bar R} \otimes {\bf\it A}^\dagger

is isomorphic to

\bf\it\bar A

.

Résumé

Soient

R

un anneau commutatif et

I

un idéal de

R

, notons

{\bf\it\bar R} : = {\bf\it R} / {\bf\it I}

. Nous prouvons que pour toute

\bf\it\bar R

-algèbre lisse

\bf\it\bar A

, il existe une

R

-algèbre lisse

A

telle que

\bf\it\bar A

est isomorphe à

{\bf\it\bar R} \otimes_{\bf\it R} {\bf\it A}

. Nous prouvons également que pour tout morphisme de

\bf\it\bar R

-algèbres lisses

{\it\bar a} : {\bf\it\bar A} \rightarrow {\bf\it\bar B}

, il existe un morphisme de

R

-algèbres lisses

{\it a} : {\bf\it A} \rightarrow {\bf\it B}

tel que

{\bf 1} \otimes {\it a} : {\bf\it\bar R} \otimes {\bf\it A} \rightarrow {\bf\it\bar R} \otimes {\bf\it B}

est isomorphe à

{\it\bar a} : {\bf\it\bar A} \rightarrow {\bf\it\bar B}

. On en déduit, lorsque

R

est nmthérien, que pour toute

\bf\it\bar R

-algèbre lisse

\bf\it\bar A

, il existe une

R

-algèbre faiblement complète très lisse

{\bf\it A}^\dagger

telle que

{\bf\it\bar R} \otimes {\bf\it A}^\dagger

est isomorphe à

\bf\it\bar A

.
Perceived Genetic Risks for Bipolar Disorder in a Patient Population: An Exploratory Study
Authors: Quaid Kimberly A. , Aschen Sharon R. , Smiley Carrie L. , Nurnberger John I. 
Published: December 2002

AbstractThirty‐one subjects with bipolar illness completed a questionnaire about genetic risk for bipolar disorder. Subjects estimated both quantitative and qualitative genetic risk for bipolar disorder for the following categories: general population, siblings, parents, spouses, and children. Results showed that quantitative risks were inflated when compared to qualitative risks and that subjects routinely overestimated the risk for developing bipolar illness. These findings suggest that genetic counseling may be useful for this population.
Accomplishments and challenges in literature data mining for biology
Authors: Hirschman Lynette , Park Jong C. , Tsujii Junichi , Wong Limsoon , Wu Cathy H. 
Published: December 2002

Abstract
We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.
Contact: wuc@georgetown.edutsujii@is.s.u-tokyo.ac.jppark@nlp.kaist.ac.krlynette@mitre.orglimsoon@lit.org.sg
* To whom correspondence should be addressed.
Computational antisense oligo prediction with a neural network model
Authors: Chalk Alistair M. , Sonnhammer Erik L. L. 
Published: December 2002

Abstract
Motivation: The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model.
Results: We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10−8). This model can predict effective AOs (&gt;50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction.
Availability: A prediction server is available at http://www.cgb.ki.se/AOpredict
Contact: alistair.chalk@cgb.ki.se
* To whom correspondence should be addressed.
Comparing expression profiles of genes with similar promoter regions
Authors: Park Peter J. , Butte Atul J. , Kohane Isaac S. 
Published: December 2002

Abstract
Motivation: Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.
Results: We use the data sets from experiments on Saccharomyces cerevisiae. Our similarity measure for the promoter regions is based on the set of common mapped or putative transcription factor binding sites and other regulatory elements in the upstream region of the genes, as contained in the Saccharomyces cerevisiae Promoter Database. We pair up the genes with high similarity scores and compare their expression levels in time-course experiment data. We find that genes with similar promoter regions on the average have significantly higher correlation, but it can vary widely depending on the genes. This confirms that the presence of similar regulatory elements often does not correspond to similarity in expression profiles and indicates that finding transcription factor binding sites or other regulatory elements starting with the expression patterns may be limited in many cases. Regardless of the correlation, the degree to which the profiles agree under different experimental conditions can be examined to derive hypotheses concerning the role of common regulatory elements. Overall, we find that considering the relationship between the promoter regions and the expression profiles starting with the regulatory elements is a difficult but useful process that can provide valuable insights.
Contact: peter_park@harvard.edu
* To whom correspondence should be addressed.
Robust estimators for expression analysis
Authors: Hubbell Earl , Liu Wei-Min , Mei Rui 
Published: December 2002

Abstract
Motivation: We consider the problem of estimating values associated with gene expression from oligonucleotide arrays. Such estimates should linearly track concentration, yield non-negative results, have statistical guarantees of robustness against outliers, and allow estimates of significance and variance.
Results: A hierarchy of simple models is used to design robust estimators meeting these goals for both stand alone and comparative experiments. This algorithm has been validated against an extensive panel of known spike experiments, and shows comparable performance to existing standards.
Availability: Algorithms available commercially as part of the MAS 5.0 software package. Data sets available from the Affymetrix website.
Contact: earl_hubbell@affymetrix.com
Supplemental Information: http://www.affymetrix.com/community/publications/affymetrix/index.affx
* To whom correspondence should be addressed.
Analysis of high density expression microarrays
with signed-rank call algorithms
Authors: Liu W.-m , Mei R. , Di X. , Ryder T. B. , Hubbell E. , Dee S. , Webster T. A. , Harrington C. A. , Ho M.-h. , Baid J. , Smeekens S. P. 
Published: December 2002

Abstract
Motivation: We consider the detection of expressed genes and the comparison of them in different experiments with the high-density oligonucleotide microarrays. The results are summarized as the detection calls and comparison calls, and they should be robust against data outliers over a wide target concentration range. It is also helpful to provide parameters that can be adjusted by the user to balance specificity and sensitivity under various experimental conditions.
Results: We present rank-based algorithms for making detection and comparison calls on expression microarrays. The detection call algorithm utilizes the discrimination scores. The comparison call algorithm utilizes intensity differences. Both algorithms are based on Wilcoxon's signed-rank test. Several parameters in the algorithms can be adjusted by the user to alter levels of specificity and sensitivity. The algorithms were developed and analyzed using spiked-in genes arrayed in a Latin square format. In the call process, p-values are calculated to give a confidence level for the pertinent hypotheses. For comparison calls made between two arrays, two primary normalization factors are defined. To overcome the difficulty that constant normalization factors do not fit all probe sets, we perturb these primary normalization factors and make increasing or decreasing calls only if all resulting p-values fall within a defined critical region. Our algorithms also automatically handle scanner saturation.
Availability: These algorithms are available commercially as part of the MAS 5.0 software package.
Contact: wei-min_liu@affymetrix.com
* To whom correspondence should be addressed.
† Current address: Mail Code CR145, Oregon Health Sciences University, Portland, OR 79201
Between-group analysis of microarray data
Authors: Culhane Aedín C. , Perrière Guy , Considine Elizabeth C. , Cotter Thomas G. , Higgins Desmond G. 
Published: December 2002

Abstract
Motivation: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data.
Results: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.
Availability: The methods described are implemented in ADE-4 which runs under MacOS and Windows, and is freely available at http://pbil.univ-lyon1.fr/ADE-4/. All scripts are available on request.
Contact: A.Culhane@ucc.ie
Supplementary information: Supplementary figures and tables are available at http://bioinfo.ucc.ie/BGA/.
* To whom correspondence should be addressed.
Calculation of the minimum number of replicate spots required
for detection of significant gene expression fold change in
microarray experiments
Authors: Black Michael A. , Doerge R. W. 
Published: December 2002

Abstract
Motivation: We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques.
Results: After analyzing two experimental data sets containing control array data, we were able to determine the statistical power available for the detection of significant differential expression given differing levels of replication. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.
Availability: The methods presented here are based on standard techniques, and are available in most statistical software packages. We used the software R to perform the computations for this work.
Contact: doerge@purdue.edu; blackma@stat.purdue.edu
* To whom correspondence should be addressed at Department of Statistics, 1399 Mathematical Sciences Building, Purdue University, West Lafayette, IN 47907-1399, USA.
A decomposition model to track gene expression signatures: preview on
observer-independent classification of ovarian cancer
Authors: Martoglio Ann-Marie , Miskin James W. , Smith Stephen K. , MacKay David J. C. 
Published: December 2002

Abstract
Motivation: A number of algorithms and analytical models have been employed to reduce the multidimensional complexity of DNA array data and attempt to extract some meaningful interpretation of the results. These include clustering, principal components analysis, self-organizing maps, and support vector machine analysis. Each method assumes an implicit model for the data, many of which separate genes into distinct clusters defined by similar expression profiles in the samples tested. A point of concern is that many genes may be involved in a number of distinct behaviours, and should therefore be modelled to fit into as many separate clusters as detected in the multidimensional gene expression space. The analysis of gene expression data using a decomposition model that is independent of the observer involved would be highly beneficial to improve standard and reproducible classification of clinical and research samples.
Results: We present a variational independent component analysis (ICA) method for reducing high dimensional DNA array data to a smaller set of latent variables, each associated with a gene signature. We present the results of applying the method to data from an ovarian cancer study, revealing a number of tissue type-specific and tissue type-independent gene signatures present in varying amounts among the samples surveyed. The observer independent results of such molecular analysis of biological samples could help identify patients who would benefit from different treatment strategies. We further explore the application of the model to similar high-throughput studies.
Availability: Supporting details of the decomposition model can be found at http://www.inference.phy.cam.ac.uk/mackay/abstracts/icagenes.html and the ovarian cancer study data can be found at http://www.path.cam.ac.uk/~angio/publications/martoglioetal2002/ovcaica.html.
Contact: amm53@cam.ac.uk; mackay@mrao.cam.ac.uk
* To whom correspondence should be addressed.
† These authors contributed equally to this work.
Partial least squares proportional hazard regression for
application to DNA microarray survival data
Authors: Nguyen Danh V. , Rocke David M. 
Published: December 2002

Abstract
Motivation: Microarrays are increasingly used in cancer research. When gene transcription data from microarray experiments also contains patient survival information, it is often of interest to predict the survival times based on the gene expression. In this paper we consider the well-known proportional hazard (PH) regression model for survival analysis. Ordinarily, the PH model is used with a few covariates and many observations (subjects). We consider here the case that the number of covariates, p, exceeds the number of samples, N, a setting typical of gene expression data from DNA microarrays.
Results: For a given vector of response values which are survival times and p gene expressions (covariates) we examine the problem of how to predict the survival probabilities, when N ≪ p. The approach taken to cope with the high dimensionality is to reduce the dimension using partial least squares with the response variable as the vector of survival times. After dimension reduction, the extracted PLS gene components are then used as covariates in a PH regression to predict the survival probabilities. We demonstrate the use of the methodology on two cDNA gene expression data sets, both containing survival data. The first data set contains 40 diffuse large B-cell lymphoma (DLBCL) tissue samples and the second data set contains 49 tissue samples from patients with locally advanced breast cancer in a prospective study.
Availability: The methodology can be implemented using a combination of standard statistical methods, available, for example, in SAS. Sample SAS macro codes to implement the methods will be available at http://stat.tamu.edu/~dnguyen/supplemental.html
Contact: dnguyen@stat.tamu.edudmrocke@ucdavis.edu
* To whom correspondence should be addressed.
Modeling the percolation of annotation errors in a
database of protein sequences
Authors: Gilks Walter R. , Audit Benjamin , De Angelis Daniela , Tsoka Sophia , Ouzounis Christos A. 
Published: December 2002

Abstract
Public sequence databases contain information on the sequence, structure and function of proteins. Genome sequencing projects have led to a rapid increase in protein sequence information, but reliable, experimentally verified, information on protein function lags a long way behind. To address this deficit, functional annotation in protein databases is often inferred by sequence similarity to homologous, annotated proteins, with the attendant possibility of error. Now, the functional annotation in these homologous proteins may itself have been acquired through sequence similarity to yet other proteins, and it is generally not possible to determine how the functional annotation of any given protein has been acquired. Thus the possibility of chains of misannotation arises, a process we term ‘error percolation’. With some simple assumptions, we develop a dynamical probabilistic model for these misannotation chains. By exploring the consequences of the model for annotation quality it is evident that this iterative approach leads to a systematic deterioration of database quality.
Contact: WRG: wally.gilks@mrc-bsu.cam.ac.uk; BA and CAO: audit@ebi.ac.uk; ouzounis@ebi.ac.uk
* To whom correspondence should be addressed.
† Both these authors contributed equally to this work.