Comet: An open‐source MS/MS sequence database search tool

Proteomics research routinely involves identifying peptides and proteins via MS/MS sequence database search. Thus the database search engine is an integral tool in many proteomics research groups. Here, we introduce the Comet search engine to the existing landscape of commercial and open‐source database search tools. Comet is open source, freely available, and based on one of the original sequence database search tools that has been widely used for many years.     

Shedding light on black boxes in protein identification

Performing a well thought‐out proteomics data analysis can be a daunting task, especially for newcomers to the field. Even researchers experienced in the proteomics field can find it challenging to follow existing publication guidelines for MS‐based protein identification and characterization in detail. One of the primary goals of bioinformatics is to enable any researcher to interpret the vast amounts of data generated in modern biology, by providing user‐friendly and robust end‐user applications, clear documentation, and corresponding teaching materials. In that spirit, we here present an extensive tutorial for peptide and protein identification, available at http://compomics.com/bioinformatics‐for‐proteomics . The material is completely based on freely available and open‐source tools, and has already been used and refined at numerous international courses over the past 3 years. During this time, it has demonstrated its ability to allow even complete beginners to intuitively conduct advanced bioinformatics workflows, interpret the results, and understand their context. This tutorial is thus aimed at fully empowering users, by removing black boxes in the proteomics informatics pipeline.     

OVNIp: An open source application facilitating the interpretation,the validation and the edition of proteomics data generated by MS analyses and de novo sequencing

Several academic software are available to help the validation and reporting of proteomics data generated by MS analyses. However, to our knowledge, none of them have been conceived to meet the particular needs generated by the study of organisms whose genomes are not sequenced. In that context, we have developed OVNIp, an open‐source application which facilitates the whole process of proteomics results interpretation. One of its unique attributes is its capacity to compile multiple results (from several search engines and/or several databank searches) with a resolution of conflicting interpretations. Moreover, OVNIp enables automated exploitation of de novo sequences generated from unassigned MS/MS spectra leading to higher sequence coverage and enhancing confidence in the identified proteins. The exploitation of these additional spectra might also identify novel proteins through a MS‐BLAST search, which can be easily ran from the OVNIp interface. Beyond this primary scope, OVNIp can also benefit to users who look for a simple standalone application to both visualize and confirm MS/MS result interpretations through a simple graphical interface and generate reports according to user‐defined forms which may integrate the prerequisites for publication. Sources, documentation and a stable release for Windows are available at http://wwwappli.nantes.inra.fr:8180/OVNIp .     

MS Data Miner: A web-based software tool to analyze, compare, and share mass spectrometry protein identifications

Data processing and analysis of proteomics data are challenging and time consuming. In this paper, we present MS Data Miner (MDM) (http://sourceforge.net/p/msdataminer), a freely available web-based software solution aimed at minimizing the time required for the analysis, validation, data comparison, and presentation of data files generated in MS software, including Mascot (Matrix Science), Mascot Distiller (Matrix Science), and ProteinPilot (AB Sciex). The program was developed to significantly decrease the time required to process large proteomic data sets for publication. This open sourced system includes a spectra validation system and an automatic screenshot generation tool for Mascot-assigned spectra. In addition, a Gene Ontology term analysis function and a tool for generating comparative Excel data reports are included. We illustrate the benefits of MDM during a proteomics study comprised of more than 200 LC-MS/MS analyses recorded on an AB Sciex TripleTOF 5600, identifying more than 3000 unique proteins and 3.5 million peptides.     

Optimizing search conditions for the mass fingerprint-based identification of proteins

The two central problems in protein identification by searching a protein sequence collection with MS data are the optimal use of experimental information to allow for identification of low abundance proteins and the accurate assignment of the probability that a result is false. For comprehensive MS-based protein identification, it is necessary to choose an appropriate algorithm and optimal search conditions. We report a systematic study of the quality of PMF-based protein identifications under different sequence collection search conditions using the Probability algorithm, which assigns the statistical significance to each result. We employed 2244 PMFs from 2-DE-separated human blood plasma proteins, and performed identification under various search constraints: mass accuracy (0.01-0.3 Da), maximum number of missed cleavage sites (0-2), and size of the sequence collection searched (5.6 x 10(4)-1.8 x 10(5)). By counting the number of significant results (significance levels 0.05, 0.01, and 0.001) for each condition, we demonstrate the search condition impact on the successful outcome of proteome analysis experiments. A mass correction procedure utilizing mass deviations of albumin matching peptides was tested in an attempt to improve the statistical significance of identifications and iterative searching was employed for identification of multiple proteins from each PMF.     

MALDI-TOF MS技术在侵袭性丝状真菌快速鉴定中的应用

探索不同培养基、不同培养时间和不同蛋白质提取方法对侵袭性丝状真菌质谱鉴定准确率的影响, 旨在提高基质辅助激光解析电离飞行时间质谱技术鉴定侵袭性丝状真菌的准确率。

采用分子生物学方法为金标准, 同时运用基质辅助激光解析电离飞行时间质谱技术对所收集临床丝状真菌进行鉴定。根据分子生物学的鉴定结果, 去除VITEK-MS v3.0数据库中没有的菌株, 其余菌株接种在沙氏葡萄糖琼脂(SDA)、马铃薯葡萄糖琼脂(PDA)和察氏培养基(CA)3种不同的培养基中, 采用2种不同的蛋白质提取方法(甲酸乙腈法和磁珠法), 获得了不同培养时间点(2、3、5、7和9 d)的特异性质谱指纹图谱。

不同丝状真菌蛋白质提取方法进行比较, 甲酸乙腈法总鉴定准确率为79.8%, 磁珠法总鉴定准确率为77.5%, 2种丝状真菌蛋白质提取方法的质谱鉴定准确率差异无统计学意义(χ²=1.040, P=0.308)。不同培养基进行比较, SDA培养基总鉴定准确率为90.7%, PDA培养基总鉴定准确率为81.4%, CA培养基总鉴定准确率为67.4%, 3种不同培养基的丝状真菌质谱鉴定准确率差异有统计学意义(χ²=36.609, P < 0.001), 其中使用SDA培养基鉴定准确率最高, 使用CA培养基鉴定准确率最低(SDA vs PDA, χ²=7.748, P=0.005;SDA vs CA, χ²=35.131, P < 0.001;PDA vs CA, χ²=10.994, P=0.001)。不同培养时间进行比较, 丝状真菌培养2、3、5、7和9 d后的质谱总鉴定准确率分别为64.3%、88.4%、89.1%、79.1%和78.3%, 不同培养时间的质谱鉴定准确率差异有统计学意义(χ²=32.274, P < 0.001)。培养3 d和培养5 d的质谱鉴定准确率优于培养7 d和培养9 d的质谱鉴定准确率, 培养2 d的质谱鉴定准确率明显低于其他时间(3 d vs 5 d, χ²=0.039, P=0.844;7 d vs 9 d, χ²=0.023, P=0.879;3 d vs 7 d, χ²=4.095, P=0.043;2 d vs 9 d, χ²=6.139, P=0.013)。

侵袭性丝状真菌使用SDA培养基培养3 d, 运用甲酸乙腈法提取蛋白质进行质谱鉴定最理想。

     

A posteriori quality control for the curation and reuse of public proteomics data

Proteomics is a rapidly expanding field encompassing a multitude of complex techniques and data types. To date much effort has been devoted to achieving the highest possible coverage of proteomes with the aim to inform future developments in basic biology as well as in clinical settings. As a result, growing amounts of data have been deposited in publicly available proteomics databases. These data are in turn increasingly reused for orthogonal downstream purposes such as data mining and machine learning. These downstream uses however, need ways to a posteriori validate whether a particular data set is suitable for the envisioned purpose. Furthermore, the (semi-)automatic curation of repository data is dependent on analyses that can highlight misannotation and edge conditions for data sets. Such curation is an important prerequisite for efficient proteomics data reuse in the life sciences in general. We therefore present here a selection of quality control metrics and approaches for the a posteriori detection of potential issues encountered in typical proteomics data sets. We illustrate our metrics by relying on publicly available data from the Proteomics Identifications Database (PRIDE), and simultaneously show the usefulness of the large body of PRIDE data as a means to derive empirical background distributions for relevant metrics.     

The effect of interfering ions on search algorithm performance for electron‐transfer dissociation data

Collision‐activated dissociation and electron‐transfer dissociation (ETD) each produce spectra containing unique features. Though several database search algorithms (e.g. SEQUEST, MASCOT, and Open Mass Spectrometry Search Algorithm) have been modified to search ETD data, this consists chiefly of the ability to search for c‐ and z^?‐ions; additional ETD‐specific features are often unaccounted for and may hinder identification. Removal of these features via spectral processing increased total search sensitivity by ～20% for both human and yeast data sets; unique peptide identifications increased by ～17% for the yeast data sets and ～16% for the human data set.     

鸟枪法蛋白质鉴定质量控制方法研究进展

鸟枪法串联质谱蛋白质鉴定策略由于其高可靠和高效率而被广泛应用于蛋白质组学研究中,这种方法直接对蛋白质混合物进行酶切,以肽段为鉴定单元,继而推导真实的样品蛋白质．由于利用质谱图推导肽段存在一定的假阳性率,而且直接对蛋白质混合物的酶切也导致了肽段和蛋白质之间关联信息的丢失,所鉴定的蛋白质难免存在部分不可靠结果．因此,蛋白质鉴定的质量控制在蛋白质组学研究中极为重要．蛋白质鉴定的质量控制包含两大类主要方法,其一为利用肽段进行蛋白质组装,当前最常用也被证明最有效的方法是使用简约原则,即用最少的蛋白质解释所有鉴定肽段,现有的方法可以分为布尔型和概率型,其二为鉴定蛋白质的可靠性评估,包括单个蛋白质鉴定置信度和蛋白质鉴定整体水平的假阳性率计算．综合各种可辅助蛋白质鉴定的先验信息,构建普适的概率统计模型,是目前蛋白质鉴定质量控制方法的发展趋势．     

Proteomics strategies for protein identification

The information from genome sequencing provides new approaches for systems-wide understanding of protein networks and cellular function. DNA microarray technologies have advanced to the point where nearly complete monitoring of gene expression is feasible in several organisms. An equally important goal is to comprehensive survey cellular proteomes and profile protein changes under different cellular states. This presents a complex analytical problem, due to the chemical variability between proteins and peptides. Here, we discuss strategies to improve accuracy and sensitivity of peptide identification, distinguish represented protein isoforms, and quantify relative changes in protein abundance.     

作物种质资源品质性状鉴定评价现状与展望

作物种质资源品质性状鉴定评价是作物种质资源研究的重要方面,是深入挖掘、广泛利用作物种质资源的基础。本文对近年来作物种质资源品质性状鉴定评价情况进行了回顾,总结了鉴定评价工作取得的主要进展:完成了近20万份的作物种质资源(约占保存总数的50%)主要营养品质性状的初步鉴定评价;作物种质资源品质鉴定评价内容涉及面广、鉴定的品质性状变异性大、多样性丰富;提高了作物种质资源品质性状鉴定评价标准化程度。此外还介绍了国际上有关品质性状鉴定的发展趋势,并对未来国内作物种质资源品质性状鉴定评价提出了意见和建议。     

FragmentationAnalyzer: An open‐source tool to analyze MS/MS fragmentation data

A thorough understanding of the fragmentation processes in MS/MS can be a powerful tool in assessing the resulting peptide and protein identifications. We here present the freely available, open‐source FragmentationAnalyzer tool ( http://fragmentation‐analyzer.googlecode.com ) that makes it straightforward to analyze large MS/MS data sets for specific types of identified peptides, using a common set of peptide properties. This enables the detection of fragmentation pattern nuances related to specific instruments or due to the presence of post‐translational modifications.     

Progress and Challenge in Computational Identification of Influenza Virus Reassortment

Virologica Sinica - Genomic reassortment is an important evolutionary mechanism for influenza viruses. In this process, the novel viruses acquire new characteristics by the exchange of the intact...     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

MassSieve: Panning MS/MS peptide data for proteins

We present MassSieve, a Java‐based platform for visualization and parsimony analysis of single and comparative LC‐MS/MS database search engine results. The success of mass spectrometric peptide sequence assignment algorithms has led to the need for a tool to merge and evaluate the increasing data set sizes that result from LC‐MS/MS‐based shotgun proteomic experiments. MassSieve supports reports from multiple search engines with differing search characteristics, which can increase peptide sequence coverage and/or identify conflicting or ambiguous spectral assignments.     

Drug metabolite profiling and identification by high-resolution mass spectrometry

Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.     

噬菌体受体及其鉴定方法

目的噬菌体又称细菌病毒,它可以侵入细菌使细菌裂解。噬菌体受体位于宿主细胞表面,能被噬菌体识别并特异性结合。噬菌体受体多种多样,包括细菌膜蛋白、LPS、磷壁酸,也可以是菌毛、鞭毛、荚膜多糖等。噬菌体在微生态平衡、微生物控制等方面具有重要意义,为了更好地研究噬菌体和挖掘噬菌体潜在价值,确定噬菌体受体是十分关键的一步。本研究就细菌噬菌体受体进行分类介绍,并对噬菌体受体鉴定方法进行总结。     

A chemometric approach to the quality control of Sutherlandia (cancer bush)

Sutherlandia frutescens (Fabaceae) commonly known as cancer-bush, is a well-known traditional phytomedicine in South Africa used to treat a range of ailments. There is limited information available on the phytochemistry and chemical variation within and between the S. frutescens and Sutherlandia microphylla species complex. This paper aims to elucidate the chemical variation of phytoconstituents (other than the non-protein amino acids) between the two species S. frutescens and S. microphylla and also between the wild and cultivated varieties of S. frutescens. An UPLC–MS analysis in tandem with chemometric analysis has been performed to assess the metabolite content of aerial plant parts obtained from different populations. Principal component analysis (PCA) was performed to observe groupings and trends in the data matrix. An orthogonal partial least square discriminant analysis (OPLS-DA) was performed which resulted in clear groups between the two taxa. Several flavonoid and triterpenoid glycoside derivatives contribute to the quantitative chemotypic variation within and between the species as observed. The identification of these compounds using advanced chromatographic techniques (UPLC–MS) and chemometric analysis leads to a better understanding of the phytochemical variation of Sutherlandia which can aid in quality control of raw material, phytomedicines and commercial herbal products.     

The OMSSAPercolator: An automated tool to validate OMSSA results

Protein identification by MS/MS is an important technique in proteome studies. The Open Mass Spectrometry Search Algorithm (OMSSA) is an open‐source search engine that can be used to identify MS/MS spectra acquired in these experiments. Here, we present a software tool, termed OMSSAPercolator, which interfaces OMSSA with Percolator, a post‐search machine learning method for rescoring database search results. We demonstrate that it outperforms the standard OMSSA scoring scheme, and provides reliable significant measurements. OMSSAPercolator is programmed using JAVA and can be readily used as a standalone tool or integrated into existing data analysis pipelines. OMSSAPercolator is freely available and can be downloaded at http://sourceforge.net/projects/omssapercolator/ .