Protein–protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of ∼99.95% and executes 16 000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29 589 high confidence interactions of ∼2 × 107 possible pairs. Of these, 14 438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro‐based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre‐Kharif (March‐May), Kharif (May‐October), and Rabi (December‐February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961‐2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north and northwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh.

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Droughts are more damaging when they occur during crop growing season. This research assessed the spatial distribution of drought risks to crops in Bangladesh. Catastrophe theory-based weighting method was used to estimate drought hazard, exposure, and risk by avoiding potential human bias. Ten major crops, including eight different types of rice, wheat, and potato, were selected for evaluation of drought risk. Results showed that 32.4%, 27.2%, and 16.2% of land in Bangladesh is prone to extreme Kharif (May-October), Rabi (November-April), and pre-Kharif (March-May) droughts, respectively. Among the major crops, Hybrid Boro rice cultivated in 18.2% of the area is found to be highly vulnerable to droughts, which is followed by High Yield Varity (HYV) Boro (16.9%), Transplant Aman (16.4%), HYV Aman (14.1%), and Basic Aman (12.4%) rice. Hybrid Boro rice in 12 districts, different varieties of Aman rice in 10 districts, and HYV Boro rice in 9 districts, mostly located in the north and northwest of Bangladesh, are exposed to high risk of droughts. High frequency of droughts and use of more land for agriculture have made the region highly prone to droughts. The methodology adopted in this study can be utilized for unbiased estimation of drought risk in agriculture in order to adopt necessary risk reduction measures.