Comprehensive Time-Course Transcriptome Reveals the Crucial Biological Pathways Involved in the Seasonal Branch Growth in Siberian Elm (Ulmus pumila).

Timber, the most prevalent organic material on this planet, is the result of a secondary xylem emerging from vascular cambium. Yet, the intricate processes governing its seasonal generation are largely a mystery. To better understand the cyclic growth of vascular tissues in elm, we undertook an extensive study examining the anatomy, physiology, and genetic expressions in Ulmus pumila. We chose three robust 15-year-old elm trees for our study. The cultivars used in this study were collected from the Inner Mongolia Autonomous Region in China and nurtured in the tree farm of Shandong Normal University. Monthly samples of 2-year-old elm branches were taken from the tree from February to September. Marked seasonal shifts in elm branch vascular tissues were observed by phenotypic observation: In February, the cambium of the branch emerged from dormancy, spurring growth. By May, elms began generating secondary xylem, or latewood, recognized by its tiny pores and dense cell structure. From June to August, there was a marked increase in the thickness of the secondary xylem. Transcriptome sequencing provides a potential molecular mechanism for the thickening of elm branches and their response to stress. In February, the tree enhanced its genetic responses to cold and drought stress. The amplified expression of CDKB, CYCB, WOX4, and ARF5 in the months of February and March reinforced their essential role in the development of the vascular cambium in elm. Starting in May, the elm deployed carbohydrates as a carbon resource to synthesize the abundant cellulose and lignin necessary for the formation of the secondary wall. Major genes participating in cellulose (SUC and CESA homologs), xylan (UGD, UXS, IRX9, IRX10, and IRX14), and lignin (PAL, C4H, 4CL, HCT, C3H, COMT, and CAD) biosynthetic pathways for secondary wall formation were up-regulated by May or/and June. In conclusion, our findings provided a foundation for an in-depth exploration of the molecular processes dictating the seasonal growth of elm timber.

[Current research status and application prospect of numerical simulation in traditional Chinese medicine drying].

With the rapid development of computer technology, numerical simulation has gradually become an important method to study drying process and improve drying equipment. Using computer to simulate the drying process of traditional Chinese medicine(TCM) is characterized by intuitiveness, scientificity, and low cost, which serves as an auxiliary means for technical innovation in TCM drying. This paper summarizes the theories of different drying methods and the research status of numerical simulation in drying, introduces the modeling methods and software of numerical simulation, and expounds the significance of numerical simulation modeling in shortening the research and development cycle, improving drying equipment, and optimizing drying parameters. However, the current numerical simulation method for drying process has problems, such as low accuracy, lack of quantitative indicators for the control of simulation results on the process, and insufficient in-depth research on the mechanism of drug quality changes. Furthermore, this paper put forward the application prospect of numerical simulation in TCM drying, providing reference for the further study of numerical simulation in this field.

Aestuariibaculum lutulentum sp. nov., a marine bacterium isolated from coastal sediment in Beihai.

A Gram-stain negative, strictly aerobic, and rod-shaped bacterium, designated as strain L182T, was isolated from coastal sediment in Beihai, Guangxi Province, PR China. Colonies of strain L182T were yellow, 2 mm in diameter, round, opaque, smooth and convex after incubation on marine ager at 30 °C for 3 days. Cells were catalase-positive but oxidase-negative. Growth of strain L182T was observed at 4-40 °C (optimum, 25 °C), pH 5.5-10.0 (optimum, pH 5.5-8.0) and with 0-6% (w/v) NaCl (optimum, 0.5-4.0%). The G + C content based on the genome sequence was 36.0%. The only respiratory quinone was MK-6. The main polar lipids included phosphatidylethanolamine, phosphatidylglycerol, one unidentified aminophospholipid, one unidentified glycolipids, four unidentified aminolipids and six unidentified lipids. The major fatty acids (> 10%) were iso-C15:0, iso-C15:1 G and iso-C17:0 3-OH. The 16S rRNA gene sequence similarity between strain L182T and Aestuariibaculum suncheonense SC17T was 98.2%, and the similarities with other type strains of the genus Aestuariibaculum were 96.1-97.2%. The average nucleotide identity and in silicon DNA-DNA hybridization values between the strain L182T and its closely related Aestuariibaculum species were 80.8-85.2% and 22.0-29.5%. According to the above results, Aestuariibaculum lutulentum sp. nov. was proposed as a novel species. The type strain is L182T (= MCCC 1K08065T = KCTC 92530T).

Microbulbifer zhoushanensis sp. nov., Microbulbifer sediminum sp. nov. and Microbulbifer guangxiensis sp. nov., three marine bacteria isolated from a tidal flat.

Three strains, TT30T, TT37T and L3T, were isolated from tidal flat samples. Cells were Gram-stain-negative, non-motile and rod shaped. Cells of strains TT30T and TT37T were able to grow in a medium containing 1.0-15.0 % (w/v) NaCl (optimum, 3.0 and 4.0 %, respectively), and cells of strain L3T was able to grow in a medium containing 1.0-10.0 % (w/v) NaCl (optimum, 1.0 %). Growth of the three strains was observed at pH 6.0-10.0 and at 10-40 °C. Strains TT30T, TT37T and L3T showed the highest similarity to Microbulbifer hydrolyticus DSM 11525T (97.7 %), M. yueqingensis CGMCC 1.10658T (98.0 %) and M. elongatus DSM 6810T (97.9 %), respectively. Results of phylogenetic analyses indicated that the three isolates represented two distinct lineages within the genus Microbulbifer. The DNA G+C contents of strains TT30T, TT37T and L3T were 61.3, 60.9 and 60.2%, respectively. The average nucleotide identity and in silico DNA-DNA hybridization values among strains TT30T, TT37T and L3T and the reference strains were 84.4-87.4 and 19.6-28.9 %, respectively. Differential phenotypic properties, chemotaxonomic differences, phylogenetic distinctiveness, together with the genomic data, demonstrated that strains TT30T, TT37 T and L3T represent novel species of the genus Microbulbifer, which are named Microbulbifer zhoushanensis sp. nov. (TT30T=KCTC 92167T=MCCC 1K07276T), Microbulbifer sediminum sp. nov. (TT37T=KCTC 92168T=MCCC 1K07277T) and Microbulbifer guangxiensis sp. nov. (L3T=KCTC 92165T=MCCC 1K07278T).

Reclassification of Parvularcula flava as Aquisalinus luteolus nom. nov. and emended description of the genus Aquisalinus.

Parvularcula flava was proposed as a novel member of genus Parvularcula in 2016. Some time earlier, Aquisalinus flavus has been proposed as a novel species of a novel genus named Aquisalinus. When comparing the 16S rRNA gene sequences of type strains P. flava NH6-79T and A. flavus D11M-2T, they showed 97.9 % sequence identity, much higher than the sequence identities 92.7-94.3 % between P. flava NH6-79T and type strains in the genus Parvularcula, indicating that the later proposed novel taxon Parvularcula flava need reclassification. The phylogenetic trees based on 16S rRNA gene sequences and genome sequences both showed that P. flava NH6-79T and A. flavus D11M-2T formed a separated branch away from strains in the genera Parvularcula, Marinicaulis and Amphiplicatus. The average amino acid identity and average nucleotide identity values of P. flava NH6-79T and A. flavus D11M-2T were 87.9 and 85.0 %, respectively, much higher than the values between P. flava NH6-79T and other closely related type strains (54.3 %-58.1 % and 68.6-70.4 %, respectively). P. flava NH6-79T and A. flavus D11M-2T also contained summed feature 8 (C18 : 1 ω6c and/or C18 : 1 ω7c) and C16 : 0 as major fatty acids, distinguishing them from other closely related taxa. Based on the results of the phylogenetic, comparative genomic and phenotypic analyses, Parvularcula flava should be reclassified as Aquisalinus luteolus nom. nov. and the description of genus Aquisalinus is emended.

Retraction of "Quorum Sensing System in Bacteroides thetaiotaomicron Strain Identified by Genome Sequence Analysis".

[This retracts the article DOI: 10.1021/acsomega.0c03986.].

Marinomonas vulgaris sp. nov., a marine bacterium isolated from seawater in a coastal intertidal zone of Zhoushan island.

A Marinomonas-like, Gram-stain-negative, strictly aerobic and rod to ovoid-shaped bacterium, designated as strain A79T, was isolated from the seawater mixtures of oyster shells and brown algae in a coastal intertidal zone of Zhoushan, China. The strain was positive for oxidase and catalase. Colonies grown on marine agar for 48 h were round, milky white, smooth and moist with the diameter of 2-3 mm. Growth was observed at 15-30 °C (optimum, 25℃), pH 5.5-9.5 (optimum, pH 8.5) and with 0.5-8% (w/v) NaCl (optimum, 2-2.5%). The G + C content based on the genome sequence was 46.0%. The only respiratory quinone was Q-8. The main polar lipids contained phosphatidylglycerol, phosphatidylethanolamine, unidentified glycolipids, unidentified phospholipid and three unidentified lipids. The major fatty acids (> 10%) were C16:0, Summed feature 3 (comprising C16:1 ω6c and/or C16:1 ω7c) and summed feature 8 (comprising C18:1 ω6c and/or C18:1 ω7c). The 16S rRNA gene sequence similarity between strain A79T and Marinomonas pollencensis IVIA-Po-185T was 97.4%, the similarities with other type strains of the genus Marinomonas were 93.8-96.7%. Based on the results, Marinomonas vulgaris sp. nov. was proposed as a novel species. The type strain is A79T (= MCCC 1K05799T = KCTC 82519T = JCM 34473T).

Aestuariibaculum sediminum sp. nov., a marine bacterium isolated from a tidal flat in Zhoushan.

A Gram-staining-negative, non-motile, strictly aerobic bacterium, designated as strain TT11T, was isolated from a sediment sample of a tidal flat connected in Zhoushan, China. Cells of strain TT11T are spherical, halotolerant, catalase- and oxidase-positive, and produce carotenoid-like pigments. Colonies were 0.5-1.0 mm diameter, smooth, round, convex and orange-yellow after growth on marine agar at 30 °C for 24 h. Growth of the strain TT11T was observed at 10-40 °C (optimum, 35 °C), at pH 6.0-9.5 (optimum, pH 6.5), and in the presence of 0-8.0% (w/v) NaCl (optimum, 0.5-1.0%). The results of 16S rRNA gene sequence analysis revealed that strain TT11T represents a member of the genus Aestuariibaculum and was closely related to Aestuariibaculum suncheonense SC17T (97.2%) and Aestuariibaculum marinum IP7T (96.8%). The G + C content of the genome was 34.6%. The only respiratory quinone was MK-6. The major fatty acids (> 10%) were iso-C15:0, iso-C15:1 G and iso-C17:0 3-OH. The major polar lipids contained phosphatidylethanolamine, phosphoglycolipid, four unidentified aminolipids, four unidentified lipids and two unidentified glycolipids. On the basis of these genomic, chemotaxonomic and phenotypic characteristics, we propose a novel species Aestuariibaculum sediminum sp. nov. with the type strain TT11T (= KCTC 82195T = MCCC 1K04734T).

Genome sequence and annotation of Bacteroides sp aff. Thetaiotaomicron strain isolated from blood.

This study is focused on genome sequence and annotation of the Bacteroides strain isolated from the blood of a patient with descending colon cancer. According to a comparison of the 16S ribosomal RNA sequence with the National Center for Biotechnology Information database, this strain was identified as Bacteroides sp. aff. Thetaiotaomicron. The next-generation sequencing of the strain was performed in a GENEWIZ laboratory (Jiangsu, China) on Illumina HiSeq device. According to CAZy classification, metabolic pathways related to carbohydrate metabolism of this strain engage the following enzymes: 427 glycosylhydrolases, 277 glycosyltransferases, 137 carbohydrate-binding modules, 48 carbohydrate esterases, and 24 polysaccharide lyases. According to the KEGG pathway database, Bacteroides sp. aff thetaiotaomicron strain is reported to incorporate 199 pathway associated genes. Bacteroides sp. aff. Thetaiotaomicron exposes the capacity of metabolizing a variety of polysaccharides. Its genome is enriched with an expanded repertoire of enzymes for the hydrolysis of glycosidic bonds and, thus, likely to hydrolyze most of glycosidic bonds in biological polysaccharides. The advanced capabilities of the studied strain to recognize and respond to environmental signals are expressed in the rich representation of one- and two-component signal transduction systems.

Quorum Sensing System in Bacteroides thetaiotaomicron Strain Identified by Genome Sequence Analysis.

This study is a bioinformatics assay on the microbial genome of Bacteroides thetaiotaomicron. The study focuses on the problem of quorum sensing as a result of adverse factors such as chemotherapy and antibiotic therapy. In patients with severe intestinal diseases, two strains of microorganisms were identified that were distinguished as new. Strains were investigated by conducting genome sequencing. The current concepts concerned with the quorum sensing system regulation by stationary-phase sigma factor and their coregulation of target genes in B. thetaiotaomicron were considered. The study suggested using bioinformatics data for the diagnosis of gastrointestinal disorders. In the course of the study, 402 genes having a greater than twofold change were identified with the 95% confidence level. The shortest and longest coding genes were predicted; the noncoding genes were detected. Biological pathways (KEGG pathways) were classified into the following categories: cellular processes, environmental information processing, genetic information processing, human disease, metabolism, and organismic systems. Among notable changes in the biofilm population observed in parallel to the planktonic B. thetaiotaomicron was the expression of genes in the polysaccharide utilization loci that were involved in the synthesis of O-glycans.

Association of OPG gene polymorphism with susceptibility to rheumatoid arthrits in Chinese Han.

AIM: To investigate the association of osteoproterin (OPG) gene polymorphisms 163A/G (rs3102735), 245T/G (rs3134069) with susceptibility to rheumatoid arthritis (RA) in Chinese Han population. OBJECTIVE: To study the correlation between the disease of rheumatoid arthritis (RA) in Chinese Han group and the association of osteoproterin (OPG) gene polymorphisms 163A/G(rs3102735) and 245T/G (rs3134069). Approaches: 205 RA patients and 171 healthy control subjects were participated into this study. Genotype analysis was conducted by polymerase chain reaction-based restriction fragment length polymorphism and was subsequently confirmed by DNA sequencing. Odd ration (OR) and 95% confidence intervals (95% CI) were calculated for the risk of genotype and allele. CONSEQUENCES: OPG gene polymorphisms 163A/G, 245T/G conformed to the Hardy-Weinberg equilibrium. The statistical differences in genotype of AA, AG, GG at 163A/G locus were founded in RA and controls. The G allele was associated with an increased risk of RA, with OR 1.219 (95% CI: 1.066-2.339). According to the observation, there are no significant differences between the RA and control groups with respect to genotype and allele frequencies of OPG gene 245T/G (χ(2)=0.734, 0.518, p>0.05). CONCLUSION: The OPG gene 163A/G SNP may be associated with the susceptibility of RA, G allele may be the risk factor for the development of RA.

Swfoldrate: predicting protein folding rates from amino acid sequence with sliding window method.

Protein folding is the process by which a protein processes from its denatured state to its specific biologically active conformation. Understanding the relationship between sequences and the folding rates of proteins remains an important challenge. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. In this study, the long-range and short-range contact in protein were used to derive extended version of the pseudo amino acid composition based on sliding window method. This method is capable of predicting the protein folding rates just from the amino acid sequence without the aid of any structural class information. We systematically studied the contributions of individual features to folding rate prediction. The optimal feature selection procedures are adopted by means of combining the forward feature selection and sequential backward selection method. Using the jackknife cross validation test, the method was demonstrated on the large dataset. The predictor was achieved on the basis of multitudinous physicochemical features and statistical features from protein using nonlinear support vector machine (SVM) regression model, the method obtained an excellent agreement between predicted and experimentally observed folding rates of proteins. The correlation coefficient is 0.9313 and the standard error is 2.2692. The prediction server is freely available at http://www.jci-bioinfo.cn/swfrate/input.jsp.

iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins.

By introducing the "multi-layer scale", as well as hybridizing the information of gene ontology and the sequential evolution information, a novel predictor, called iLoc-Gpos, has been developed for predicting the subcellular localization of Gram positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gpos-mPLoc was adopted to demonstrate the power of iLoc-Gpos. The dataset contains 519 Gram-positive bacterial proteins classified into the following four subcellular locations: (1) cell membrane, (2) cell wall, (3) cytoplasm, and (4) extracell; none of proteins included has ≥25% pairwise sequence identity to any other in a same subset (subcellular location). The overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gpos was over 93%, which is about 11% higher than that by GposmPLoc. As a user-friendly web-server, iLoc-Gpos is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc- Gpos or http://www.jci-bioinfo.cn/iLoc-Gpos. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user � s convenience, the iLoc-Gpos web-server also has the function to accept the batch job submission, which is not available in the existing version of Gpos-mPLoc web-server.

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.

Although numerous efforts have been made for predicting the subcellular locations of proteins based on their sequence information, it still remains as a challenging problem, particularly when query proteins may have the multiplex character, i.e., they simultaneously exist, or move between, two or more different subcellular location sites. Most of the existing methods were established on the assumption: a protein has one, and only one, subcellular location. Actually, recent evidence has indicated an increasing number of human proteins having multiple subcellular locations. This kind of multiplex proteins should not be ignored because they may bear some special biological functions worthy of our attention. Based on the accumulation-label scale, a new predictor, called iLoc-Hum, was developed for identifying the subcellular localization of human proteins with both single and multiple location sites. As a demonstration, the jackknife cross-validation was performed with iLoc-Hum on a benchmark dataset of human proteins that covers the following 14 location sites: centrosome, cytoplasm, cytoskeleton, endoplasmic reticulum, endosome, extracellular, Golgi apparatus, lysosome, microsome, mitochondrion, nucleus, peroxisome, plasma membrane, and synapse, where some proteins belong to two, three or four locations but none has 25% or higher pairwise sequence identity to any other in the same subset. For such a complicated and stringent system, the overall success rate achieved by iLoc-Hum was 76%, which is remarkably higher than that by any of the existing predictors that also have the capacity to deal with this kind of system. Further comparisons were also made via two independent datasets; all indicated that the success rates by iLoc-Hum were even more significantly higher than its counterparts. As a user-friendly web-server, iLoc-Hum is freely accessible to the public at or . For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results by choosing either a straightforward submission or a batch submission, without the need to follow the complicated mathematical equations involved.

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.

Predicting protein subcellular localization is a challenging problem, particularly when query proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-location proteins. Actually, multiple-location proteins should not be ignored because they usually bear some special functions worthy of our notice. By introducing the "multi-labeled learning" approach, a new predictor, called iLoc-Plant, has been developed that can be used to deal with the systems containing both single- and multiple-location plant proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Plant on a benchmark dataset of plant proteins classified into the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole, where some proteins belong to two or three locations but none has ≥ 25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Plant was 71%, which is remarkably higher than those achieved by any existing predictors that also have the capacity to deal with such a stringent and complicated plant protein system. As a user-friendly web-server, iLoc-Plant is freely accessible to the public at the web-site or . Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematic equations presented in this paper for its integrity. It is anticipated that iLoc-Plant may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, Systems Biology, and Drug Development.

A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites.

Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the "multi-label scale" and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg. The dataset contains 1,392 gram-negative bacterial proteins classified into the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. Of the 1,392 proteins, 1,328 are each with only one subcellular location and the other 64 are each with two subcellular locations, but none of the proteins included has pairwise sequence identity to any other in a same subset (subcellular location). It was observed that the overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gneg was over 91%, which is about 6% higher than that by Gneg-mPLoc. As a user-friendly web-server, iLoc-Gneg is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Gneg. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Gneg web-server also has the function to accept the batch job submission, which is not available in the existing version of Gneg-mPLoc web-server. It is anticipated that iLoc-Gneg may become a useful high throughput tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development.

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites.

In the last two decades or so, although many computational methods were developed for predicting the subcellular locations of proteins according to their sequence information, it is still remains as a challenging problem, particularly when the system concerned contains both single- and multiple-location proteins. Also, among the existing methods, very few were developed specialized for dealing with viral proteins, those generated by viruses. Actually, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very important because it is closely related to their destructive tendencies and consequences. In this paper, by introducing the "multi-label scale" and by hybridizing the gene ontology information with the sequential evolution information, a predictor called iLoc-Virus is developed. It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3) host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. The iLoc-Virus predictor not only can more accurately predict the location sites of viral proteins in a host cell, but also have the capacity to deal with virus proteins having more than one location. As a user-friendly web-server, iLoc-Virus is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Virus. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Virus web-server also has the function to accept the batch job submission. It is anticipated that iLoc-Virus may become a useful high throughput tool for both basic research and drug development.

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins.

Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or "singleplex" proteins. Actually, multiple-location or "multiplex" proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the "multi-labeled learning" and "accumulation-layer scale", a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.

2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids.

Introduction of graphic representation for biological sequences can provide intuitive overall pictures as well as useful insights for performing large-scale analysis. Here, a new two-dimensional graph, called "2D-MH", is proposed to represent protein sequences. It is formed by incorporating the information of the side-chain mass of each of the constituent amino acids and its hydrophobicity. The graphic curve thus generated is featured by (1) an one-to-one correspondence relation without circuit or degeneracy, (2) better reflecting the innate structure of the protein sequence, (3) clear visibility in displaying the similarity of protein sequences, (4) more sensitive for the mutation sites important for drug targeting, and (5) being able to be used as a metric for the "evolutionary distance" of a protein from one species to the other. It is anticipated that the presented graphic method may become a useful vehicle for large-scale analysis of the avalanche of protein sequences generated in the post-genomic age. As a web-server, 2D-MH is freely accessible at http://icpr.jci.jx.cn/bioinfo/pplot/2D-MH, by which one can easily generate the two-dimensional graphs for any number of protein sequences and compare the evolutionary distances between them.