Annotation and BAC/PAC localization of nonredundant ESTs from drought-stressed seedlings of anindica rice

To decipher the genes associated with drought stress response and to identify novel genes in rice, we utilized 1540 high-quality expressed sequence tags (ESTs) for functional annotation and mapping to rice genomic sequences. These ESTs were generated earlier by 3'-end single-pass sequencing of 2000 cDNA clones from normalized cDNA libraries constructed from drought-stressed seedlings of anindica rice. A rice UniGene set of 1025 transcripts was constructed from this collection through the BLASTN algorithm. Putative functions of 559 nonredundant ESTs were identified by BLAST similarity search against public databases. Putative functions were assigned at a stringency E value of 10-6 in BLASTN and BLASTX algorithms. To understand the gene structure and function further, we have utilized the publicly available finished and unfinished rice BAC/PAC (BAC, bacterial artificial chromosome; PAC, P1 artificial chromosome) sequences for similarity search using the BLASTN algorithm. Further, 603 nonredundant ESTs have been mapped to BAC/PAC clones. BAC clones were assigned by a homology of above 95% identity along 90% of EST sequence length in the aligned region. In all, 700 ESTs showed rice EST hits in GenBank. Of the 325 novel ESTs, 128 were localized to BAC clones. In addition, 127 ESTs with identified putative functions but with no homology in IRGSP (International Rice Genome Sequencing Program) BAC/PAC sequences were mapped to the Chinese WGS (whole genome shotgun contigs) draft sequence of the rice genome. Functional annotation uncovered about a hundred candidate ESTs associated with abiotic stress in rice andArabidopsis that were previously reported based on microarray analysis and other studies. This study is a major effort in identifying genes associated with drought stress response and will serve as a resource to rice geneticists and molecular biologists

Identification of stress-responsive genes in an indica rice (Oryza sativa L.) using ESTs generated from drought-stressed seedlings

The impacts of drought on plant growth and development limit cereal crop production worldwide. Rice (Oryza sativa) productivity and production is severely affected due to recurrent droughts in almost all agroecological zones. With the advent of molecular and genomic technologies, emphasis is now placed on understanding the mechanisms of genetic control of the drought-stress response. In order to identify genes associated with water-stress response in rice, ESTs generated from a normalized cDNA library, constructed from drought-stressed leaf tissue of an indica cultivar, Nagina 22 were used. Analysis of 7794 cDNA sequences led to the identification of 5815 rice ESTs. Of these, 334 exhibited no significant sequence homology with any rice ESTs or full-length cDNAs in public databases, indicating that these transcripts are enriched during drought stress. Analysis of these 5815 ESTs led to the identification of 1677 unique sequences. To characterize this drought transcriptome further and to identify candidate genes associated with the drought-stress response, the rice data were compared with those for abiotic stress-induced sequences obtained from expression profiling studies in Arabidopsis, barley, maize, and rice. This comparative analysis identified 589 putative stress-responsive genes (SRGs) that are shared by these diverse plant species. Further, the identified leaf SRGs were compared to expression profiles for a drought-stressed rice panicle library to identify common sequences. Significantly, 125 genes were found to be expressed under drought stress in both tissues. The functional classification of these 125 genes showed that a majority of them are associated with cellular metabolism, signal transduction, and transcriptional regulation

Functional genomics of drought stress response in rice: transcript mapping of annotated unigenes of an indica rice (Oryza sativa L. cv. Nagina 22)

Rice being one of the widely cultivated cereals across diverse agroecological systems, is prone to high yield losses due to recurring droughts. In India, drought is a major constraint of rice production and accounts for as much as 15% of yield losses during some years. Conventional plant breeding techniques though cumbersome and time-consuming, have been immensely helpful in releasing drought-tolerant varieties. However, this is not adequate to cope up with the future demand for rice, as drought seems to spread to more regions and seasons across the country. Understanding the genes that govern rice plant architecture and response to drought stress is urgently needed to enhance breeding rice with improved drought tolerance. In order to identify genes associated with drought stress response and their temporal and spatial regulation, we took the genomic approach. By generating a large set of expressed sequence tags (ESTs) from cDNA libraries of drought-stressed seedlings and transcript profiling, we identified 589 genes presumed to be involved in drought stress. These 5814 ESTs are assembled into 2094 contigs and localized onto chromosome arms. We present here the physical map of the 2094 unigene set along with 589 annotated putative stress responsive genes of rice. Further, using ESTs, a few of drought quantitative trait loci (QTLs) have been dissected and putative candidate genes identified. This will be useful to rice researchers as ready reference source for breeding through developing candidate gene markers, molecular dissection of QTLs associated with drought stress and map-based cloning

Smart phone based snoring sound analysis to identify upper airway obstructions

Obstructive sleep apnea (OSA) is characterized by upper airway obstructions known as apnea/hypopnea events. Narrowing of the upper airway during or near the vicinity of apnea/hypopnea causes the spectrum of the snores to shift to higher frequencies. Using an instrumentation quality wideband (WB) microphone (4Hz-100kHz), we previously demonstrated that potentially diagnostically useful frequency shifts could be detected even in regions beyond the human hearing range. WB-microphone based systems are expensive and not available for home use or population screening application. In this paper we explore the feasibility of using smart phones to analyze snoring sounds in the 20Hz-22kHz band to identify events of upper airway obstructions. Modern smart phones have internal microphones with bandwidths up to 22kHz, above the nominal human hearing range, and provide a good platform for sound acquisition and processing. For the work of this paper we used a Samsung Galaxy S3 phone and recorded overnight respiratory sound data from 8 patients undergoing routine Polysomnography (PSG) study in a hospital. Our target was to develop models to classify each standard 30 second epoch of data as non-apnea or apnea. Using 700 epochs we developed logistic regression models with the input as snoring sound features and the outputs as the diagnostic classification of each event (apnea/non-apnea). Models developed within a 20Hz-15kHz band had accuracies of 89-93%, sensitivities 70-78% and kappa index ranging 0.75-0.83 on validation data set. When the same models were developed on the 20Hz-22kHz frequency band the improved performance shows accuracies 94-97%, sensitivities 93-100%, and kappa ranging 0.86-0.91. The study shows that smart phones based high frequency band (15-22kHz) of snoring sounds carry information about the upper airway obstructions. Our non-contact, smart phone based snoring sound technology has potential to identify upper airway obstructions