谈系统发生树建立的分子标准

随着进化生物学以及生物信息学的发展 ,研究不同物种间进化关系的方法也有了新的进展。其中分子进化方法使得系统发生树的建立更加简便和精确。几种系统发生树的建树方法及相关分子标准目前比较流行。。     

贝叶斯一致性分析法在全基因组系统发生树分析上的应用

构建系统发生树时,其拓扑结构会在不同的基因组区域产生不一致性。对此问题,贝叶斯一致性分析法(BCA)可在全基因组规模上进行系统发生树分析,并进而对不一致性信息进行量化统计。采用此方法对由C3H/Hu小鼠(Mus musculus)和129Sv小鼠回交多代产生的129S1小鼠进行系统发生树分析,输入相应的一组序列文件,用若干生物信息学软件(如VCFtools,Repeat Masker,PAUP*4.0,Mr Model Test,Mr Bayes等)对其进行屏蔽重复序列、序列比对等处理,辅以Perl语言脚本,最终得到全基因组范围不同区段系统发生树不一致信息。在小鼠10号染色体的所有99个基因座中,支持129S1和129Sv品系小鼠为姐妹关系的拓扑结构占了84.7%(后验概率最高),这证明了C3H/Hu小鼠对129S1小鼠基因组的贡献程度较小。结果表明,贝叶斯一致性分析法有助于基因组不同区段进化历史的研究。     

自发性树鼩乳腺肿瘤的特性(英文)

乳腺癌是严重危害女性健康的常见恶性肿瘤,建立合适的乳腺癌动物模型对于研究人类乳腺癌的生物学机制及发展新的防治方法至关重要。相对于常用的啮齿类动物,树鼩(Tupaia belangeri chinensis,tree shrew)因在进化层次上更接近于人类而可用于建立更适合的乳腺癌模型。该文详细了介绍一例树鼩自发性乳头状良性乳腺肿瘤。免疫组化结果显示该例肿瘤孕激素受体阳性且Ki-67阳性细胞比例显著增加;而活化的Caspase3阳性细胞比例较低;且肿瘤的形态和病理与人导管内乳头状肿瘤非常接近。提示利用树鼩建立乳腺肿瘤模型的可行性。     

肿瘤染色体畸变分析方法新进展

摘要: 肿瘤的发生多与染色体畸变有关, 确定染色体畸变与肿瘤的关系, 必然离不开染色体畸变的检测分析。文章简要综述几种常用染色体畸变的检测方法及其新进展, 包括G显带、荧光原位杂交(FISH )、光谱核型分析(SKY)、多色荧光原位杂交(M-FISH)、多色显带分析技术(Rx-FISH)、比较基因组杂交(CGH)和微阵列比较基因组杂交(Array CGH), 以及这些方法在肿瘤诊断和研究方面的应用。     

分子进化研究中系统发生树的重建

在现代分子进化研究中,根据现有生物基因或物种多样性来重建生物的进化史是一个非常重要的问题。一个可靠的系统发生的推断,将揭示出有关生物进化过程的顺序,有助于我们了解生物进化的历史和进化机制。本文简要介绍了系统发生树推断的几个重要问题：建树方法、数据转换、树的可靠性及目前使用较多的几种分析软件。     

生物多样性起源与进化研究进展

生物多样性的起源与进化是生命科学领域最重要的科学问题之一。多组学数据的积累和相关分析技术的发展, 极大地推动了人们对生物多样性起源与进化的理解和研究, 使得阐明生物进化事件发生的过程与机制成为可能。值此《生物多样性》创刊30周年之际, 本文简要回顾生物多样性起源与进化相关研究在近年来取得的重要研究进展, 以期帮助读者了解该研究方向的发展现状。过去10年中, 生物多样性起源与进化相关研究在生命之树重建、生物多样性时空分布格局、物种概念、物种形成与适应性进化以及新性状起源与多样化等方面取得了许多重要进展, 并在此基础上厘清了许多分类单元间的系统发育关系、揭示了生物多样性分布格局的部分历史成因、提出了新的物种概念和物种形成模型、阐明了新性状和新功能发生的部分分子机制。我们认为, 更精准地重建生命之树、深入挖掘基因组数据以及多学科交叉融合将是今后生物多样性研究的主要趋势。     

mtDNA基因树拓扑距离比较和基因分群

基因树间拓扑距离数据的比较进一步证明：与分割拓扑距离相比,能经拓扑距离是一种更为精确的测度,利用相对通经拓扑距离构建了8个基因的拓扑距离树。基因的拓扑距离树能直观地反映不同基因树的拓扑结构差异大小,可用来对基因进行分群。此外,发现不同DNA序列用于构建多基因树中其系统发生信息存在“累加”,“合取”,“含盖”,“相斥”等数学关系。这可解释在mtDNA基因组中一些基因比另一些基因更适合用来的构建树的结果。结果提示从GenBank中应选择具有累加基因的DNA序列或蛋白质氨基酸序列合并来构建物种。在讨论中还提出了一种获得真树的新建树策略。     

印度血桐与中平树基因组调查及SSR分子标记分析

印度血桐与中平树是大戟科血桐属植物,该属植物具有多种药用价值,被广泛应用于民间医学中许多疾病的治疗,这两种植物种子中含有的神经酸也引起了研究者的高度关注。为确定适合印度血桐与中平树的全基因组测序研究策略,该研究采用二代高通量测序技术,结合生物信息学的方法首次测定了印度血桐与中平树的基因组大小、杂合率、重复率等基因组信息并初步分析了两种材料的SSR序列特征。结果表明:(1)印度血桐与中平树的基因组大小分别为986.84和946.23 M。(2)印度血桐与中平树的杂合率分别为0.75%和0.65%,重复序列比例分别为73.02%和71.5%。(3)通过对2种材料基因组序列的SSR特征分析,在印度血桐中共鉴定了4 499 185个SSR,在中平树中共鉴定了4 969 098个SSR。该研究结果为印度血桐与中平树SSR分子标记的筛选、开发以及全基因组深度测序提供了理论指导。     

U83 Box C/D snoRNA构建果蝇科系统发生树

利用已报道的黑腹果蝇U83基因搜索果蝇基因数据库,鉴定了10种新的果蝇科U83同源基因,它们均位于相应物种核蛋白基因rpl3的内含子中。以冈比亚按蚊为外类群,对11种果蝇的U83核苷酸序列作进化关系分析,用邻接法重建了系统发生树,结果与传统方法构建的系统发生树相比,能反映果蝇科的大致进化关系,但还存在部分差别。为增加序列信息,把序列长度拓展至整个U83所在的内含子,同法构建系统发生树,结果与传统系统发生树几乎完全一致。该研究是用boxC/D snoRNA基因序列构建系统发生树的首次尝试,实验结果证明U83可以很好地用于构建果蝇科内各物种的种系发生树。     

树鼩模型在人类病毒性疾病研究中的应用进展

由于树鼩在进化上接近于灵长类动物,在生理、生化及解剖学等生物学特性方面与人类有着相似之处,树鼩得到越来越多的关注,研究人员运用与其他动物相比具有多种优势的树鼩建立了一系列的疾病模型,如病毒类疾病、神经系统、肿瘤等,本文着重就树鼩在人类病毒疾病方面的研究进展进行概述。     

Distance-based reconstruction of tree models for oncogenesis.

Comparative genomic hybridization (CGH) is a laboratory method to measure gains and losses in the copy number of chromosomal regions in tumor cells. It is hypothesized that certain DNA gains and losses are related to cancer progression and that the patterns of these changes are relevant to the clinical consequences of the cancer. It is therefore of interest to develop models which predict the occurrence of these events, as well as techniques for learning such models from CGH data. We continue our study of the mathematical foundations for inferring a model of tumor progression from a CGH data set that we started in Desper et al. (1999). In that paper, we proposed a class of probabilistic tree models and showed that an algorithm based on maximum-weight branching in a graph correctly infers the topology of the tree, under plausible assumptions. In this paper, we extend that work in the direction of the so-called distance-based trees, in which events are leaves of the tree, in the style of models common in phylogenetics. Then we show how to reconstruct the distance-based trees using tree-fitting algorithms developed by researchers in phylogenetics. The main advantages of the distance-based models are that 1) they represent information about co-occurrences of all pairs of events, instead of just some pairs, 2) they allow quantitative predictions about which events occur early in tumor progression, and 3) they bring into play the extensive methodology and software developed in the context of phylogenetics. We illustrate the distance-based tree method and how it complements the branching tree method, with a CGH data set for renal cancer.     

A linked series of laboratory exercises in molecular biology utilizing bioinformatics and GFP

Molecular biologists commonly use bioinformatics to map and analyze DNA and protein sequences and to align different DNA and protein sequences for comparison. Additionally, biologists can create and view 3D models of protein structures to further understand intramolecular interactions. The primary goal of this 10-week laboratory was to introduce the importance of bioinformatics in molecular biology. Students employed multiprimer, site-directed mutagenesis to create variant colors from a plasmid expressing green fluorescent protein (GFP). Isolated mutant plasmid from Escherichia coli showing changes in fluorescence were sequenced. Students used sequence alignment tools, protein translator tools, protein modeling, and visualization to analyze the potential effect of their mutations within the protein structure. This laboratory linked molecular techniques and bioinformatics to promote and expand the understanding of experimental results in an upper-level undergraduate laboratory course.     

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.     

Comparative genomic hybridisation for the analysis of chromosomal imbalances in solid tumours and haematological malignancies

 Comparative genomic hybridisation (CGH) is based on a two-colour, competitive fluorescence in situ hybridisation of differentially labelled tumour and reference DNA to normal metaphase chromosomes. This new technology has made a great impact in molecular tumour pathology due to its possible application to archival specimens and the ability to create copy number karyotypes throughout the whole genome from very small amounts of DNA. If chromosomal imbalances can be correlated with a etiological and clinical features of tumours, CGH could be able to provide new prognostic and diagnostic criteria. CGH findings further provide starting points for the molecular genetic characterisation of altered chromosomal regions harbouring yet unidentified genes involved in tumorigenesis and tumour progression. An overview of the results of published CGH studies on solid tumours and haematological malignancies is presented. Methodological limitations of the CGH technology are reported, as well as future developments which will improve its use in routine analysis. Accepted: 29 July 1997     

Cutaneous melanoma: fishing with chips

DNA microarray technology is a versatile platform that allows rapid genetic analysis to take place on a genome-wide scale and has revolutionized the way cancers are studied. This platform has enabled researchers to characterize mechanisms central to tumorigenesis and understand important molecular events in the multi-step tumor progression model of cutaneous melanoma and other cancers. In melanoma, multiple global gene expression profiling studies using various DNA microarray platforms and various experimental designs have been performed. Each study has been able to capture and characterize either the involvement of a novel pathway or a novel cause-effect-relationship. The use of microarrays to define subclasses, to identify differentially regulated genes within a mutational context to analyze epigenetically regulated genes has resulted in an unprecedented understanding of the biology of cutaneous melanoma that may lead to more accurate diagnosis, more comprehensive prognosis, prediction and more effective therapeutic interventions. Related DNA microarray platforms like array-comparative genomic hybridization (CGH) have also been instrumental to identify many non-random chromosomal alterations; however, studies identifying validated targets as a result of CGH are limited. Thus, there exists significant opportunity to discover novel melanoma genes and translate such discoveries into meaningful clinical endpoints. In this review, we focus on various DNA microarray-based studies performed in cutaneous melanoma and summarize our current understanding of the genetics and biology of melanoma progression derived from accumulating genomic information.     

Mixture models as a method to find present and divergent genes in comparative genomic hybridization studies on bacteria

Comparative genomic hybridization (CGH) using microarrays is performed on bacteria in order to test for genomic diversity within various bacterial species. The microarrays used for CGH are based on the genome of a fully sequenced bacterium strain, denoted reference strain. Labelled DNA fragments from a sample strain of interest and from the reference strain are hybridized to the array. Based on the obtained ratio intensities and the total intensities of the signals, each gene is classified as either present (one copy or multiple copies) or divergent (zero copies). In this paper mixture models with different number of components are tted on different combinations of variables and compared with each other. The study shows that mixture models fitted on both the ratio intensities and the total intensities including the replicates for each gene improve, compared to previously published methods, the results for several of the data sets tested. Some summaries of the data sets are proposed as a guide for the choice of model and the choice of number of components. The models are applied on data from CGH experiments with the bacteria Staphylococcus aureus and     

Inferring tree models for oncogenesis from comparative genome hybridization data.

Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer. We derive a tree model inference algorithm based on the idea of a maximum-weight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.     

Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.     

Conceptual data modelling for bioinformatics

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.     

A molecular phylogeny of enteric bacteria and implications for a bacterial species concept

A molecular phylogeny for seven taxa of enteric bacteria (Citrobacter freundii, Enterobacter cloacae, Escherichia coli, Hafnia alvei, Klebsiella oxytoca, Klebsiella pneumoniae, and Serratia plymuthica) was made from multiple isolates per taxa taken from a collection of environmental enteric bacteria. Sequences from five housekeeping genes (gapA, groEL, gyrA, ompA, and pgi) and the 16S rRNA gene were used to infer individual gene trees and were concatenated to infer a composite molecular phylogeny for the species. The isolates from each taxa formed tight species clusters in the individual gene trees, suggesting the existence of 'genotypic' clusters that correspond to traditional species designations. These sequence data and the resulting gene trees and consensus tree provide the first data set with which to assess the utility of the recently proposed core genome hypothesis (CGH). The CGH provides a genetically based approach to applying the biological species concept to bacteria.