PaGeFinder: quantitative identification of spatiotemporal pattern genes

Evolutionary divergence of subgenomes in common carp provides insights into speciation and allopolyploid success

Hybridization and polyploidization have made great contributions to speciation, heterosis, and agricultural production within plants, but there is still limited understanding and utilization in animals. Subgenome structure and expression reorganization and cooperation post hybridization and polyploidization are essential for speciation and allopolyploid success. However, the mechanisms have not yet been comprehensively assessed in animals. Here, we produced a high-fidelity reference genome sequence for common carp, a typical allotetraploid fish species cultured worldwide. This genome enabled in-depth analysis of the evolution of subgenome architecture and expression responses. Most genes were expressed with subgenome biases, with a trend of transition from the expression of subgenome A during the early stages to that of subgenome B during the late stages of embryonic development. While subgenome A evolved more rapidly, subgenome B contributed to a greater level of expression during development and under stressful conditions. Stable dominant patterns for homoeologous gene pairs both during development and under thermal stress suggest a potential fixed heterosis in the allotetraploid genome. Preferentially expressing either copy of a homoeologous gene at higher levels to confer development and response to stress indicates the dominant effect of heterosis. The plasticity of subgenomes and their shifting of dominant expression during early development, and in response to stressful conditions, provide novel insights into the molecular basis of the successful speciation, evolution, and heterosis of the allotetraploid common carp.

A survey of transcriptome complexity using full-length isoform sequencing in the tea plant Camellia sinensis

Tea is one of the most popular beverages and its leaves are rich in catechins, contributing to the diverse flavor as well as beneficial for human health. However, the study of the post-transcriptional regulatory mechanism affecting the synthesis of catechins remains insufficient. Here, we sequenced the transcriptome using PacBio sequencing technology and obtained 63,111 full-length high-quality isoforms, including 1302 potential novel genes and 583 highly reliable fusion transcripts. We also identified 1204 lncRNAs with high quality, containing 188 known and 1016 novel lncRNAs. In addition, 311 mis-annotated genes were corrected based on the high-quality Isoseq reads. A large number of alternative splicing (AS) events (3784) and alternative polyadenylation (APA) genes (18,714) were analyzed, accounting for 8.84% and 43.7% of the total annotated genes, respectively. We also found that 2884 genes containing AS and APA features exhibited higher expression levels than other genes. These genes are mainly involved in amino acid biosynthesis, carbon fixation in photosynthetic organisms, phenylalanine, tyrosine, tryptophan biosynthesis, and pyruvate metabolism, suggesting that they play an essential role in the catechins content of tea polyphenols. Our results further improved the level of genome annotation and indicated that post-transcriptional regulation plays a crucial part in synthesizing catechins.

Comparative Study of Transcriptome in the Hearts Isolated from Mice, Rats, and Humans

The heart is a significant organ in mammalian life, and the heartbeat mechanism has been an essential focus of science. However, few studies have focused on species differences. Accordingly, challenges remain in studying genes that have universal functions across species and genes that determine species differences. Here, we analyzed transcriptome data in mouse, rat, and human atria, ventricles, and sinoatrial nodes (SA) obtained from different platforms and compared them by calculating specificity measure (SPM) values in consideration of species differences. Among the three heart regions, the species differences in SA were the greatest, and we searched for genes that determined the essential characteristics of SA, which was SHOX2 in our criteria. The SPM value of SHOX2 was prominently high across species. Similarly, by calculating SPM values, we identified 3 atrial-specific, 11 ventricular-specific, and 17 SA-specific markers. Ontology analysis identified 70 cardiac region- and species-specific ontologies. These results suggest that reanalyzing existing data by calculating SPM values may identify novel tissue-specific genes and species-dependent gene expression. This study identified the importance of SHOX2 as an SA-specific transcription factor, a novel cardiac regional marker, and species-dependent ontologies.

The genome of a mangrove plant, Avicennia marina, provides insights into adaptation to coastal intertidal habitats

Whole-genome duplication, gene family and lineage-specific genes analysis based on high-quality genome reveal the adaptation mechanisms of Avicennia marina to coastal intertidal habitats. Mangrove plants grow in a complex habitat of coastal intertidal zones with high salinity, hypoxia, etc. Therefore, it is an interesting question how mangroves adapt to the unique intertidal environment. Here, we present a chromosome-level genome of the Avicennia marina, a typical true mangrove with a size of 480.43 Mb, contig N50 of 11.33 Mb and 30,956 annotated protein-coding genes. We identified 621 Avicennia-specific genes that are mainly related to flavonoid and lignin biosynthesis, auxin homeostasis and response to abiotic stimulus. We found that A. marina underwent a novel specific whole-genome duplication, which is in line with a brief era of global warming that occurred during the paleocene-eocene maximum. Comparative genomic and transcriptomic analyses outline the distinct evolution and sophisticated regulations of A. marina adaptation to the intertidal environments, including expansion of photosynthesis and oxidative phosphorylation gene families, unique genes and pathways for antibacterial, detoxifying antioxidant and reactive oxygen species scavenging. In addition, we also analyzed salt gland secretion-related genes, and those involved in the red bark-related flavonoid biosynthesis, while significant expansions of key genes such as NHX, 4CL, CHS and CHI. High-quality genomes in future investigations will facilitate the understand of evolution of mangrove and improve breeding.

Transcriptome analysis of skin color variation during and after overwintering of Malaysian red tilapia

The commercial value of red tilapia is hampered by variations in skin color during overwintering. In this study, three types of skin of red tilapia, including the skin remained pink color during and after overwintering (P), the skin changed from pink color to black color during overwintering and remained black color after overwintering (P-B), and the skin changed from pink color to black color during overwintering but recovered to pink color when the temperature rose after overwintering (P-B-P), were used to analyze their molecular mechanisms of color variation. The transcriptome results revealed that the P, P-B, and P-B-P libraries had 43, 42, and 43 million clean reads, respectively. The top 10 abundance mRNAs and specific mRNAs (specificity measure SPM > 0.9) were screened. After comparing intergroup gene expression levels, there were 2528, 1924, and 1939 differentially expressed genes (DEGs) between P-B-P and P-B, P-B-P and P, and P-B and P, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of color-related mRNAs showed that a number of DEGs, including tyrp1, tyr, pmel, mitf, mc1r, asip, tat, hpdb, and foxd3, might play a potential role in pigmentation. Additionally, the co-expression patterns of genes were detected within the pigment-related pathways by the PPI network from P-B vs. P group. Furthermore, DEGs from the apoptosis and autophagy pathways, such as baxα, beclin1, and atg7, might be involved in the fading of red tilapia melanocytes. The findings will aid in understanding the molecular mechanism underlying skin color variation in red tilapia during and after overwintering as well as lay a foundation for future research aimed at improving red tilapia skin color characteristics.

Identification, Characterization and Function of Orphan Genes Among the Current Cucurbitaceae Genomes

Orphan genes (OGs) that are missing identifiable homologs in other lineages may potentially make contributions to a variety of biological functions. The Cucurbitaceae family consists of a wide range of fruit crops of worldwide or local economic significance. To date, very few functional mechanisms of OGs in Cucurbitaceae are known. In this study, we systematically identified the OGs of eight Cucurbitaceae species using a comparative genomics approach. The content of OGs varied widely among the eight Cucurbitaceae species, ranging from 1.63% in chayote to 16.55% in wax gourd. Genetic structure analysis showed that OGs have significantly shorter protein lengths and fewer exons in Cucurbitaceae. The subcellular localizations of OGs were basically the same, with only subtle differences. Except for aggregation in some chromosomal regions, the distribution density of OGs was higher near the telomeres and relatively evenly distributed on the chromosomes. Gene expression analysis revealed that OGs had less abundantly and highly tissue-specific expression. Interestingly, the largest proportion of these OGs was significantly more tissue-specific expressed in the flower than in other tissues, and more detectable expression was found in the male flower. Functional prediction of OGs showed that (1) 18 OGs associated with male sterility in watermelon; (2) 182 OGs associated with flower development in cucumber; (3) 51 OGs associated with environmental adaptation in watermelon; (4) 520 OGs may help with the large fruit size in wax gourd. Our results provide the molecular basis and research direction for some important mechanisms in Cucurbitaceae species and domesticated crops.

Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research

Background

Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform and reliable RGs for breast cancer research have not been identified, limiting the value of target gene expression studies. Here, we aimed to identify reliable and accurate RGs for breast cancer tissues and cell lines using the RNA-seq dataset.

Methods

First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs. Next, ten genes with relatively stable expression levels were chosen as novel candidate RGs, together with six conventional RGs. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 11 types of surgically excised breast tumor specimens (n = 66) and seven breast cancer cell lines (n = 21). Five publicly available algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the expression stability of each RG across all breast cancer tissues and cell lines.

Results

Our results show that RG combinations SF1 + TRA2B + THRAP3 and THRAP3 + RHOA + QRICH1 showed stable expression in breast cancer tissues and cell lines, respectively, and that they displayed good interchangeability. We propose that these combinations are optimal triplet RGs for breast cancer research.

Conclusions

In summary, we identified novel and reliable RG combinations for breast cancer research based on a public RNA-seq dataset. Our results lay a solid foundation for the accurate normalization of qRT-PCR results across different breast cancer tissues and cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00963-8.

Genome-Wide Identification, Characterization and Function Analysis of Lineage-Specific Genes in the Tea Plant Camellia sinensis

Genes that have no homologous sequences with other species are called lineage-specific genes (LSGs), are common in living organisms, and have an important role in the generation of new functions, adaptive evolution and phenotypic alteration of species. Camellia sinensis var. sinensis (CSS) is one of the most widely distributed cultivars for quality green tea production. The rich catechins in tea have antioxidant, free radical elimination, fat loss and cancer prevention potential. To further understand the evolution and utilize the function of LSGs in tea, we performed a comparative genomics approach to identify Camellia-specific genes (CSGs). Our result reveals that 1701 CSGs were identified specific to CSS, accounting for 3.37% of all protein-coding genes. The majority of CSGs (57.08%) were generated by gene duplication, and the time of duplication occurrence coincide with the time of two genome-wide replication (WGD) events that happened in CSS genome. Gene structure analysis revealed that CSGs have shorter gene lengths, fewer exons, higher GC content and higher isoelectric point. Gene expression analysis showed that CSG had more tissue-specific expression compared to evolutionary conserved genes (ECs). Weighted gene co-expression network analysis (WGCNA) showed that 18 CSGs are mainly associated with catechin synthesis-related pathways, including phenylalanine biosynthesis, biosynthesis of amino acids, pentose phosphate pathway, photosynthesis and carbon metabolism. Besides, we found that the expression of three CSGs (CSS0030246, CSS0002298, and CSS0030939) was significantly down-regulated in response to both types of stresses (salt and drought). Our study first systematically identified LSGs in CSS, and comprehensively analyzed the features and potential functions of CSGs. We also identified key candidate genes, which will provide valuable assistance for further studies on catechin synthesis and provide a molecular basis for the excavation of excellent germplasm resources.

Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress

Alicyclobacillus acidoterrestris is a major concern in fruit juice industry due to its spoilage potential of acidic fruit juice. Quantifying the expression levels of functional genes by real-time quantitative polymerase chain reaction (RT-qPCR) is necessary to elucidate the response mechanisms of A. acidoterrestris to acid stress. However, appropriate reference genes (RGs) for data normalization are required to obtain reliable RT-qPCR results. In this study, eight novel candidate RGs were screened based on transcriptome datasets of A. acidoterrestris under acid stress. The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. This study revealed for the first time that A. acidoterrestris had different suitable RGs under different acid conditions, with implications for further deciphering the acid response mechanisms of this spoilage-causing bacterium.

Identification, characterization and expression analysis of lineage-specific genes within mangrove species Aegiceras corniculatum

Lineage-specific genes (LSGs) are the genes that have no recognizable homology to any sequences in other species, which are important drivers for the generation of new functions, phenotypic changes, and facilitating species adaptation to environment. Aegiceras corniculatum is one of major mangrove plant species adapted to waterlogging and saline conditions, and the exploration of aegiceras-specific genes (ASGs) is important to reveal its adaptation to the harsh environment. Here, we performed a systematic analysis on ASGs, focusing on their sequence characterization, origination and expression patterns. Our results reveal that there are 4823 ASGs in the genome, approximately 11.84% of all protein-coding genes. High proportion (45.78%) of ASGs originate from gene duplication, and the time of gene duplication of ASGs is consistent with the timing of two genome-wide replication (WGD) events that occurred in A. corniculatum, and also coincides with a short period of global warming during the Paleocene-Eocene Maximum (PETM, 55.5 million years ago). Gene structure analysis showed that ASGs have shorter protein lengths, fewer exons, and higher isoelectric point. Expression patterns analysis showed that ASGs had low levels of expression and more tissue-specific expression. Weighted gene co-expression network analysis (WGCNA) revealed that 86 ASGs co-expressed gene modules were primarily involved in pathways related to adversity stress, including plant hormone signal transduction, phenylpropanoid biosynthesis, photosynthesis, peroxisome and pentose phosphate pathway. This study provides a comprehensive analysis of the characteristics and potential functions of ASGs and identifies key candidate genes, which will contribute to the subsequent further investigation of the adaptation of A. corniculatum to intertidal coastal wetland habitats.

The stage-specific long non-coding RNAs and mRNAs identification and analysis during early development of common carp, Cyprinus carpio

Cyprinus carpio is considered an alternative vertebrate fish model to zebrafish. However, systemic times-series research on the lncRNAs and mRNAs during early development of C. carpio has not been reported yet. This study provides the first long non-coding RNA (lncRNA)-mRNA expression profiles during six main early development stages (2 h post-fertilization hpf, 6 hpf, 12 hpf, 20 hpf, 64 hpf and 1 day post-hatching). A total of 51,979 lncRNAs were identified. We screened the top 10 abundance lncRNAs and mRNAs and stage-specific lncRNAs and mRNAs (specificity measure SPM > 0.9). We identified significant differentially expressed lncRNAs and mRNAs (|log2 (fold change)| ≥ 1 and false discovery rate FDR of <0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified numerous signaling pathways. Additionally, the lncRNA-mRNA co-regulated network analysis of two lncRNAs (lncrps25 and malat1) and two mRNAs (mitf and troponin T) were investigated. Our results provide new insight into the role of lncRNAs and mRNAs, and would advance the understanding of lncRNA-mediated mechanisms in early development of fish.

Comparative microRNAs expression profiles analysis during embryonic development of common carp, Cyprinus carpio

MicroRNAs (miRNAs) play important roles in biological processes by regulating specific gene expression. Limited miRNAs information is available on embryonic development in common carp (Cyprinus carpio) so far. In this study, six important embryonic development stages of C.carpio were collected to perform a times-series of small RNA-seq experiments from cleavage, blastocyst, gastrulation, organ formation, hatching stage to 1 day post-hatching larva. The expression profiles of miRNAs were identified and differentially expressed miRNAs (DEMs) were screened out based on pairwise comparison. A mean of 12,744,989 raw reads and 9,888,123 clean reads were obtained from each library. A total of 2565 miRNAs were identified. 68 of 204 DEMs were overlapped with stage-specific miRNAs, in which 15 were known miRNAs and seemed to play a key role in embryogenesis. Additionally, time-course expression reveals several intriguing fluctuations during embryogenesis. Numerous signaling pathways were identified in embryonic development, including the phototransduction, hippo signaling pathway, Wnt, melanogenesis, histidine metabolism and fatty acid biosynthesis. The results would provide new insight into the roles of miRNAs in embryonic development, and would help us to advance the understanding of miRNA-mediated mechanisms in embryonic development of fish.

Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus)

BACKGROUND

In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology.

RESULTS

Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown that NCBP3 + SDHA + PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released.

CONCLUSION

This study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3 + SDHA + PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datasets are usually with multiple missing values due to faults or distortion. Unfortunately, many established algorithms for classification require a fully complete matrix as input. Thus it is necessary to impute the missing data to increase the effectiveness of classification for datasets with a few missing values. In this paper, we compare the main methods for estimating the missing values in electrocardiogram data, e.g., the “Zero method”, “Mean method”, “PCA-based method”, and “RPCA-based method” and then propose a novel KNN-based classification algorithm, i.e., a modified kernel Difference-Weighted KNN classifier (MKDF-WKNN), which is fit for the classification of imbalance datasets. The experimental results on the UCI database indicate that the “RPCA-based method” can successfully handle missing values in arrhythmia dataset no matter how many values in it are missing and our proposed classification algorithm, MKDF-WKNN, is superior to other state-of-the-art algorithms like KNN, DS-WKNN, DF-WKNN, and KDF-WKNN for uneven datasets which impacts the accuracy of classification.

mRBPome capture identifies the RNA-binding protein TRIM71, an essential regulator of spermatogonial differentiation

Continual spermatogenesis relies on the actions of an undifferentiated spermatogonial population that is composed of stem cells and progenitors. Here, using mouse models, we explored the role of RNA-binding proteins (RBPs) in regulation of the biological activities of this population. Proteins bound to polyadenylated RNAs in primary cultures of undifferentiated spermatogonia were captured with oligo (dT)-conjugated beads after UV-crosslinking and profiled by proteomics (termed mRBPome capture), yielding a putative repertoire of 473 RBPs. From this database, the RBP TRIM71 was identified and found to be expressed by stem and progenitor spermatogonia in prepubertal and adult mouse testes. Tissue-specific deletion of TRIM71 in the male germline led to reduction of the undifferentiated spermatogonial population and a block in transition to the differentiating state. Collectively, these findings demonstrate a key role of the RBP system in regulation of the spermatogenic lineage and may provide clues about the influence of RBPs on the biology of progenitor cell populations in other lineages.

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in olfaction is relevant because these RNAs have been shown to play a role in chromatin modification and nuclear architecture reorganization, processes that accompany olfactory differentiation and olfactory receptor gene choice, one of the most poorly understood gene regulatory processes in mammals. In this study, we used a combination of in silico and ex vivo approaches to uncover a comprehensive catalogue of olfactory lncRNAs and to investigate their expression in the mouse olfactory organs. Initially, we used a novel machine-learning lncRNA classifier to discover hundreds of annotated and unannotated lncRNAs, some of which were predicted to be preferentially expressed in the main olfactory epithelium and the vomeronasal organ, the most important olfactory structures in the mouse. Moreover, we used whole-tissue and single-cell RNA sequencing data to discover lncRNAs expressed in mature sensory neurons of the main epithelium. Candidate lncRNAs were further validated by in situ hybridization and RT-PCR, leading to the identification of lncRNAs found throughout the olfactory epithelia, as well as others exquisitely expressed in subsets of mature olfactory neurons or progenitor cells.

Identification, characterization and expression analysis of lineage-specific genes within Triticeae

Lineage-specific genes (LSGs) are a set of genes in a given taxon without significant sequence similarity to genes and intergenic sequences of other taxa and are functional. The tribe Triticeae mainly includes species of different ploidy levels, such as staple food crops wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). This study is aimed at mining and characterizing the Triticeae-specific genes (TSGs) using expressed sequence data of wheat. A total of 3812 TSGs was identified and they were generally characterized by smaller size, fewer exons, shorter open reading frames and lower expression levels. Most TSGs were expressed with tissue preference and many of them were predominantly expressed in reproduction related tissues, especially in young stamen. Nearly one third of the TSGs were stress-responsive and inducible under abiotic and/or biotic stresses. A co-expression-based annotation supported the relevance of some TSGs with reproduction and stress responses, indicating their potential economic importance.

SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data

Different tissues and diseases have distinct transcriptional profilings with specifically expressed genes (SEGs). So, the identification of SEGs is an important issue in the studies of gene function, biological development, disease mechanism and biomarker discovery. However, few accurate and easy-to-use tools are available for RNA sequencing (RNA-seq) data to detect SEGs. Here, we presented SEGtool, a tool based on fuzzy c-means, Jaccard index and greedy annealing method for SEG detection automatically and self-adaptively ignoring data distribution. Testing result showed that our SEGtool outperforms the existing tools, which was mainly developed for microarray data. By applying SEGtool to Genotype-Tissue Expression (GTEx) human tissue data set, we detected 3181 SEGs with tissue-related functions. Regulatory networks reveal tissue-specific transcription factors regulating many SEGs, such as ETV2 in testis, HNF4A in liver and NEUROD1 in brain. Applied to a case study of single-cell sequencing (SCS) data from embryo cells, we identified many SEGs in specific stages of human embryogenesis. Notably, SEGtool is suitable for RNA-seq data and even SCS data with high specificity and accuracy. An implementation of SEGtool R package is freely available at http://bioinfo.life.hust.edu.cn/SEGtool/.

Detection of condition-specific marker genes from RNA-seq data with MGFR

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR’s predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

Tissue differences revealed by gene expression profiles of various cell lines

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.

Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein⁻Peptide Docking and Molecular Dynamics Simulation

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT_1290-1310) remain elusive and the interaction between GluN2B-CT_1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT_1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT_1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT_1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT_1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT_1290-1310 and DAPK1 interface.

Transcriptome-wide identification of optimal reference genes for expression analysis of Pyropia yezoensis responses to abiotic stress

Background

Pyropia yezoensis, a marine red alga, is an ideal research model for studying the mechanisms of abiotic stress tolerance in intertidal seaweed. Real-time quantitative polymerase chain reaction (RT-qPCR) is the most commonly used method to analyze gene expression levels. To accurately quantify gene expression, selection and validation of stable reference genes is required.

Results

We used transcriptome profiling data from different abiotic stress treatments to identify six genes with relatively stable expression levels: MAP, ATPase, CGS1, PPK, DPE2, and FHP. These six genes and three conventional reference genes, UBC, EF1-α, and eif4A, were chosen as candidates for optimal reference gene selection. Five common statistical approaches (geNorm, ΔCt method, NormFinder, BestKeeper, and ReFinder) were used to identify the stability of each reference gene. Our results show that: MAP, UBC, and FHP are stably expressed in all analyzed conditions; CGS1 and UBC are stably expressed under conditions of dehydration stress; and MAP, UBC, and CGS1 are stably expressed under conditions of temperature stress.

Conclusion

We have identified appropriate reference genes for RT-qPCR in P. yezoensis under different abiotic stress conditions which will facilitate studies of gene expression under these conditions.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4643-8) contains supplementary material, which is available to authorized users.

SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue

Human specifically expressed genes (SEGs) usually serve as potential biomarkers for disease diagnosis and treatment. However, the regulation underlying their specific expression remains to be revealed. In this study, we constructed SEG regulation database (SEGreg; available at http://bioinfo.life.hust.edu.cn/SEGreg) for showing SEGs and their transcription factors (TFs) and microRNA (miRNA) regulations under different physiological conditions, which include normal tissue, cancer tissue and cell line. In total, SEGreg collected 6387, 1451, 4506 and 5320 SEGs from expression profiles of 34 cancer types and 55 tissues of The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Human Body Map and Genotype-Tissue Expression databases/projects, respectively. The cancer or tissue corresponding expressed miRNAs and TFs were identified from miRNA and gene expression profiles, and their targets were collected from several public resources. Then the regulatory networks of all SEGs were constructed and integrated into SEGreg. Through a user-friendly interface, users can browse and search SEGreg by gene name, data source, tissue, cancer type and regulators. In summary, SEGreg is a specialized resource to explore SEGs and their regulations, which provides clues to reveal the mechanisms of carcinogenesis and biological processes.

Genes uniquely expressed in human growth plate chondrocytes uncover a distinct regulatory network

Background

Chondrogenesis is the earliest stage of skeletal development and is a highly dynamic process, integrating the activities and functions of transcription factors, cell signaling molecules and extracellular matrix proteins. The molecular mechanisms underlying chondrogenesis have been extensively studied and multiple key regulators of this process have been identified. However, a genome-wide overview of the gene regulatory network in chondrogenesis has not been achieved.

Results

In this study, employing RNA sequencing, we identified 332 protein coding genes and 34 long non-coding RNA (lncRNA) genes that are highly selectively expressed in human fetal growth plate chondrocytes. Among the protein coding genes, 32 genes were associated with 62 distinct human skeletal disorders and 153 genes were associated with skeletal defects in knockout mice, confirming their essential roles in skeletal formation. These gene products formed a comprehensive physical interaction network and participated in multiple cellular processes regulating skeletal development. The data also revealed 34 transcription factors and 11,334 distal enhancers that were uniquely active in chondrocytes, functioning as transcriptional regulators for the cartilage-selective genes.

Conclusions

Our findings revealed a complex gene regulatory network controlling skeletal development whereby transcription factors, enhancers and lncRNAs participate in chondrogenesis by transcriptional regulation of key genes. Additionally, the cartilage-selective genes represent candidate genes for unsolved human skeletal disorders.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4378-y) contains supplementary material, which is available to authorized users.

Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks

Protein complexes play significant roles in cellular processes. Identifying protein complexes from protein-protein interaction (PPI) networks is an effective strategy to understand biological processes and cellular functions. A number of methods have recently been proposed to detect protein complexes. However, most of methods predict protein complexes from static PPI networks, and usually overlook the inherent dynamics and topological properties of protein complexes. In this paper, we proposed a novel method, called NABCAM (Neighbor Affinity-Based Core-Attachment Method), to identify protein complexes from dynamic PPI networks. Firstly, the centrality score of every protein is calculated. The proteins with the highest centrality scores are regarded as the seed proteins. Secondly, the seed proteins are expanded to complex cores by calculating the similarity values between the seed proteins and their neighboring proteins. Thirdly, the attachments are appended to their corresponding protein complex cores by comparing the affinity among neighbors inside the core, against that outside the core. Finally, filtering processes are carried out to obtain the final clustering result. The result in the DIP database shows that the NABCAM algorithm can predict protein complexes effectively in comparison with other state-of-the-art methods. Moreover, many protein complexes predicted by our method are biologically significant.

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences

Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes

Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.

Prediction and validation of cis-regulatory elements in 5' upstream regulatory regions of lectin receptor-like kinase gene family in rice

Lectin receptor-like kinases (LecRLKs) play crucial roles in regulating plant growth and developmental processes in response to stress. In transcriptional gene regulation for normal cellular functions, cis-acting regulatory elements (CREs) direct the temporal and spatial gene expression with respect to environmental stimuli. A complete insightful of the transcriptional gene regulation system relies on effective functional analysis of CREs. Here, we analyzed the potential putative CREs present in the promoters of rice LecRLKs genes by using PlantCARE database. The CREs in LecRLKs promoters are associated with plant growth/development, light response, plant hormonal regulation processes, various stress responses, hormonal response like ABA, root-specific expression responsive, drought responsive, and cell and organ specific regulatory elements. The effect of methylation on these cis-regulatory elements was also analyzed. Real-time analysis of rice seedling under various stress conditions showed the expression levels of selected LecRLK genes superimposing the number of different CREs present in 5' upstream region. The overall results showed that the possible CREs function in the selective expression/regulation of LecRLKs gene family and during rice plant development under stress.

Analysis of the Bacterial Communities in Two Liquors of Soy Sauce Aroma as Revealed by High-Throughput Sequencing of the 16S rRNA V4 Hypervariable Region

Chinese liquor is one of the world's oldest distilled alcoholic beverages and an important commercial fermented product in China. The Chinese liquor fermentation process has three stages: making Daqu (the starter), stacking fermentation on the ground, and liquor fermentation in pits. We investigated the bacterial diversity of Maotai and Guotai Daqu and liquor fermentation using high-throughput sequencing of the V4 hypervariable region of the 16S rRNA gene. A total of 70,297 sequences were obtained from the Daqu samples and clustered into 17 phyla. The composition of the bacterial communities in the Daqu from these two soy sauce aroma-style Chinese liquors was the same, although some bacterial species changed in abundance. Between the Daqu and liquor fermentation samples, 12 bacterial phyla increased. The abundance of Lactobacillus and Pseudomonas increased in the liquor fermentation. This study has used high-throughput sequencing to provide new insights into the bacterial composition of the Chinese liquor Daqu and fermentation. Similarities in the distribution of bacteria in the soy sauce aroma-style Chinese liquors Daqu suggest that the abundance of bacteria might be generally concerned to other liquor.

Combinations of Histone Modifications for Pattern Genes

Histone post-translational modifications play important roles in transcriptional regulation. It is known that multiple histone modifications can act in a combinatorial manner. In this study, we investigated the effects of multiple histone modifications on expression levels of five gene categories (four kinds of pattern genes and non-pattern genes) in coding regions. The combinatorial patterns of modifications for the five gene categories were also studied in the regions. Our results indicated that the differences in the expression levels between any two gene categories were significant. There were some corresponding differences in multiple histone modification levels among the five gene categories. Multiple histone modifications jointly impacted expression levels of every gene category. Four mutual combinations of histone modifications were found and analyzed.

ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain

Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns. Second, standard tools of expression analysis such as matrix factorization can capture those patterns but do not explicitly incorporate spatial dependency. To overcome those limitations, we proposed a computational method to detect recurrent patterns in the spatial-temporal gene expression data of developing mouse brains. We demonstrated that regional distinction in brain development could be revealed by localized gene expression patterns. The patterns expressed in the forebrain, medullary and pontomedullary, and basal ganglia are enriched with genes involved in forebrain development, locomotory behavior, and dopamine metabolism respectively. In addition, the timing of global gene expression patterns reflects the general trends of molecular events in mouse brain development. Furthermore, we validated functional implications of the inferred patterns by showing genes sharing similar spatial-temporal expression patterns with Lhx2 exhibited differential expression in the embryonic forebrains of Lhx2 mutant mice. These analysis outcomes confirm the utility of recurrent expression patterns in studying brain development.

CrossNorm: a novel normalization strategy for microarray data in cancers

Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.

Influence factors on the correlations between expression levels of neighboring pattern genes

Some genes tend to cluster and be co-expressed. Multiple factors affect gene co-expression. In this study, we investigated the relationships between multiple factors and the correlations of expression levels of neighboring genes, which were divided into four kinds of pattern genes and one type of non-pattern gene. Our results indicate that the correlation between expression levels of neighboring non-pattern genes is related to multiple factors with the exception of transcriptional orientations of neighboring genes. The correlation between expression levels of neighboring specific genes or neighboring repressed genes is likely to be dependent on the co-functions of neighboring genes. The correlation between expression levels of neighboring housekeeping genes is associated with histone modifications in intergenic regions.

Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

Comparative proteomic analysis provides new insights into cadmium accumulation in rice grain under cadmium stress

Rice is one of the most important staple crops. During the growth season, rice plants are inevitably subjected to numerous stresses, among which heavy metal stress represented by cadmium contamination not only hindering the yield of rice but also affecting the food safety by Cd accumulating in rice grains. The mechanism of Cd accumulation in rice grains has not been well elucidated. In this study, we compare the proteomic difference between two genotypes with different Cd accumulation ability in grains. Verification of differentially expressed protein-encoding genes was analyzing by quantitative PCR (QPCR) and reanalysis of microarray expression data. Forty-seven proteins in total were successfully identified through proteomic screening. GO and KEGG enrichment analysis showed Cd accumulation triggered stress-related pathways in the cells, and strongly affecting metabolic pathways. Many proteins associated with nutrient reservoir and starch-related enzyme were identified in this study suggesting that a considerably damage on grain quality was caused. The results also implied stress response was initiated by the abnormal cells and the transmission of signals may mediated by reactive oxygen species (ROS). Our research will provide new insights into Cd accumulation in rice grain under Cd stress.

Discrimination of the expression of paralogous microRNA precursors that share the same major mature form

Background

MicroRNAs (miRNAs) are a class of small non-coding RNAs generated from endogenous transcripts that form hairpin structures. The hairpin precursor is processed into two mature miRNAs that form major/minor duplexes. Mature miRNAs regulate gene expression by cleaving mRNA or repressing protein translation. Numerous miRNAs have been discovered via deep sequencing. Many miRNAs are produced from multiple genome sites. These miRNAs are grouped into paralogous families of miRNAs that generate the same major mature form within organisms. Currently, no method of distinguishing the expression of these miRNAs is available.

Results

In the present study, strategies were developed to discriminate and quantify the expression of paralogous miRNA precursors. First, paralogous miRNA precursors that were differentially expressed in tissues were identified through analysis of the coexpression scores of their major and minor forms based on deep sequencing data. Then the precursors were identified by monitoring the expression of their host gene or minor form using real-time PCR. Finally, precursors were identified by assessing the expression of clusters of miRNA members. These approaches were used to distinguish miR-128-1 and miR-128-2 as well as miR-194-1 and miR-194-2. The mechanism of transcription related to the differential expression of miR-194-1 and miR-194-2 was also investigated.

Conclusion

This is the first report to distinguish paralogous miRNA copies by analyzing the expression of major-minor pairs, the host gene, and miRNA clusters. Discriminating paralogous precursors can provide useful information for investigating the mechanisms that regulate miRNA gene expression under different physiological and pathological conditions.

iTRAQ-based proteomic analysis of the metabolism mechanism associated with silicon response in the marine diatom Thalassiosira pseudonana

Silicon is a critical element for diatom growth; however our understanding of the molecular mechanisms involved in intracellular silicon responses are limited. In this study, an iTRAQ-LC-MS/MS quantitative proteomic approach was coupled with an established synchrony technique to reveal the global metabolic silicon-response in the model diatom Thalassiosira pseudonana subject to silicon starvation and readdition. Four samples, which corresponded to the time of silicon starvation, girdle band synthesis, valve formation, and right after daughter cell separation (0, 1, 5, 7 h), were collected for the proteomic analysis. The results indicated that a total of 1,831 proteins, representing 16% of the predicted proteins encoded by the T. pseudonana genome, could be identified. Of the identified proteins, 165 were defined as being differentially expressed proteins, and these proteins could be linked to multiple biochemical pathways. In particular, a number of proteins related to silicon transport, cell wall synthesis, and cell-cycle progress could be identified. In addition, other proteins that are potentially involved in amino acid synthesis, protein metabolism, and energy generation may have roles in the cellular response to silicon. Our findings provide a range of valuable information that will be of use for further studies of this important physiological response that is unique to diatoms.

PaGenBase: a pattern gene database for the global and dynamic understanding of gene function

Pattern genes are a group of genes that have a modularized expression behavior under serial physiological conditions. The identification of pattern genes will provide a path toward a global and dynamic understanding of gene functions and their roles in particular biological processes or events, such as development and pathogenesis. In this study, we present PaGenBase, a novel repository for the collection of tissue- and time-specific pattern genes, including specific genes, selective genes, housekeeping genes and repressed genes. The PaGenBase database is now freely accessible at http://bioinf.xmu.edu.cn/PaGenBase/. In the current version (PaGenBase 1.0), the database contains 906,599 pattern genes derived from the literature or from data mining of more than 1,145,277 gene expression profiles in 1,062 distinct samples collected from 11 model organisms. Four statistical parameters were used to quantitatively evaluate the pattern genes. Moreover, three methods (quick search, advanced search and browse) were designed for rapid and customized data retrieval. The potential applications of PaGenBase are also briefly described. In summary, PaGenBase will serve as a resource for the global and dynamic understanding of gene function and will facilitate high-level investigations in a variety of fields, including the study of development, pathogenesis and novel drug discovery.

Characterization of physiological response and identification of associated genes under heat stress in rice seedlings

Global warming, which is caused by greenhouse gas emissions, makes food crops more vulnerable to heat stress. Understanding the heat stress-related mechanisms in crops and classifying heat stress-related genes can increase our knowledge in heat-resistant molecular biology and propel developments in molecular design breeding, which can help rice cope with unfavorable temperatures. In this study, we carried out a physiological analysis of rice plants after heat stress. The results show a dramatic increase in malondialdehyde contents and SOD activities. We successfully isolated 11 heat-related rice genes with known function annotation through DNSH, which is an improved SSH method for screening long cDNA fragments. The reanalysis of microarray data from public database revealed that all these genes displayed various expression patterns after heat stress, drought, cold and salt. Quantitative real-time reverse transcription PCR was also performed to validate the expression of these genes after heat stress. The expressions in 10 genes were all significantly changed except for contig 77, which is a CBL-interacting protein kinase. Several reports have been published about the members of the same gene family.