MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of b...MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as展开更多
Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis.However,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the cu...Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis.However,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software.Here,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis.Results show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes.In comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat node.CloudLCA can be run on one multiprocessor node or a cluster.It is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following analysis.Moreover,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.展开更多
With the rapid development of computing technology, three-dimensional （3D） human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human b...With the rapid development of computing technology, three-dimensional （3D） human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.展开更多
The functional impact of several long intergenic non-coding RNAs (lincRNAs) has been characterized in previous studies. However, it is difficult to identify lincRNAs on a large-scale and to ascertain their functions o...The functional impact of several long intergenic non-coding RNAs (lincRNAs) has been characterized in previous studies. However, it is difficult to identify lincRNAs on a large-scale and to ascertain their functions or predict their structures in laboratory experiments because of the diversity, lack of knowledge and specificity of expression of lincRNAs. Furthermore, although there are a few well-characterized examples of lincRNAs associated with cancers, these are just the tip of the iceberg owing to the complexity of cancer. Here, by combining RNA-Seq data from several kinds of human cell lines with chromatin-state maps and human expressed sequence tags, we successfully identified more than 3000 human lincRNAs, most of which were new ones. Subsequently, we predicted the functions of 105 lincRNAs based on a coding-non-coding gene co-expression network. Finally, we propose a genetic mediator and key regulator model to unveil the subtle relationships between lincRNAs and lung cancer. Twelve lincRNAs may be principal players in lung tumorigenesis. The present study combines large-scale identification and functional prediction of human lincRNAs, and is a pioneering work in characterizing cancer-associated lincRNAs by bioinformatics.展开更多
Mammals and other complex organisms can transcribe an abundance of long non-coding RNAs(lncRNAs)that fulfill a wide variety of regulatory roles in many biological processes.These roles,including as scaffolds and as gu...Mammals and other complex organisms can transcribe an abundance of long non-coding RNAs(lncRNAs)that fulfill a wide variety of regulatory roles in many biological processes.These roles,including as scaffolds and as guides for protein-coding genes,mainly depend on the structure and expression level of lncRNAs.In this review,we focus on the current methods for analyzing lncRNA structure and expression,which is basic but necessary information for in-depth,large-scale analysis of lncRNA functions.展开更多
Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 end.Poly(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) ...Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 end.Poly(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) mRNA are largely unknown.Previous studies have shown that a significant proportion of gene transcripts are poly(A) or bimorphic(containing both poly(A)+ and poly(A) transcripts).We compared the expression levels of poly(A) and poly(A)+ RNA mRNAs in normal and cancer cell lines.We also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A) transcriptome sequences between a normal human mammary gland cell line(HMEC) and a breast cancer cell line(MCF-7),as well as between a normal human lung cell line(NHLF) and a lung cancer cell line(A549).The data showed that normal and cancer cell lines differentially express these two forms of mRNA.Gene ontology(GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their transcript.The data showed that cell cycle-,apoptosis-,and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts,which were also associated with the cancers.Furthermore,translational elongation and translation functions were also found for the poly(A) protein-coding genes in cancer cell lines.We demonstrate that poly(A) transcripts play an important role in cancer development.展开更多
文摘MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as
文摘Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis.However,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software.Here,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis.Results show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes.In comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat node.CloudLCA can be run on one multiprocessor node or a cluster.It is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following analysis.Moreover,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.
基金This work was supported by the Knowledge Innovation Program of the Institute of Computing Technology of the Chinese Academy of Sciences under Grant No. ICT20166040, the Science and Technology Service Network Initiative of Chinese Academy of Sciences under Grant No. KFJ-STS-ZDTP-017, the National Natural Science Foundation of China under Grant Nos. 61502453 and 61611130215, the Royal Society-Newton Mobility Grant of UK under Grant No. IE150731, and the CCP （China Computer Federation）-Tencent Open Research Fund of China under Grant No. AGR20160118.
文摘With the rapid development of computing technology, three-dimensional （3D） human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.
基金supported by Beijing Natural Science Foundation(5122029)
文摘The functional impact of several long intergenic non-coding RNAs (lincRNAs) has been characterized in previous studies. However, it is difficult to identify lincRNAs on a large-scale and to ascertain their functions or predict their structures in laboratory experiments because of the diversity, lack of knowledge and specificity of expression of lincRNAs. Furthermore, although there are a few well-characterized examples of lincRNAs associated with cancers, these are just the tip of the iceberg owing to the complexity of cancer. Here, by combining RNA-Seq data from several kinds of human cell lines with chromatin-state maps and human expressed sequence tags, we successfully identified more than 3000 human lincRNAs, most of which were new ones. Subsequently, we predicted the functions of 105 lincRNAs based on a coding-non-coding gene co-expression network. Finally, we propose a genetic mediator and key regulator model to unveil the subtle relationships between lincRNAs and lung cancer. Twelve lincRNAs may be principal players in lung tumorigenesis. The present study combines large-scale identification and functional prediction of human lincRNAs, and is a pioneering work in characterizing cancer-associated lincRNAs by bioinformatics.
基金supported by the National High Technology Research and Development Program of China(2012AA020402)National Natural Science Foundation of China(11074084,30970558)
文摘Mammals and other complex organisms can transcribe an abundance of long non-coding RNAs(lncRNAs)that fulfill a wide variety of regulatory roles in many biological processes.These roles,including as scaffolds and as guides for protein-coding genes,mainly depend on the structure and expression level of lncRNAs.In this review,we focus on the current methods for analyzing lncRNA structure and expression,which is basic but necessary information for in-depth,large-scale analysis of lncRNA functions.
基金supported in part by the National Natural Science Foundation of China (31000564,31071137,91229120)the Beijing Natural Science Foundation (5122029)the Knowledge Innovation Program of the Chinese Academy of Sciences (KSCX2-EW-R-01)
文摘Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 end.Poly(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) mRNA are largely unknown.Previous studies have shown that a significant proportion of gene transcripts are poly(A) or bimorphic(containing both poly(A)+ and poly(A) transcripts).We compared the expression levels of poly(A) and poly(A)+ RNA mRNAs in normal and cancer cell lines.We also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A) transcriptome sequences between a normal human mammary gland cell line(HMEC) and a breast cancer cell line(MCF-7),as well as between a normal human lung cell line(NHLF) and a lung cancer cell line(A549).The data showed that normal and cancer cell lines differentially express these two forms of mRNA.Gene ontology(GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their transcript.The data showed that cell cycle-,apoptosis-,and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts,which were also associated with the cancers.Furthermore,translational elongation and translation functions were also found for the poly(A) protein-coding genes in cancer cell lines.We demonstrate that poly(A) transcripts play an important role in cancer development.
