Predicting plant microRNAs based on functional and biogenesis data

鲨鱼和玫瑰是如何从一个细胞进化而来的？这个问题长期以来一直引起科学家的兴趣，因为所有的植物和动物都来自一个细胞，即受精卵。这个单细胞通过多次细胞分裂发展成一个完整的有机体，但在这些细胞分裂过程中，遗传信息并没有改变。因此，我们所有的细胞都含有相同的遗传信息。然而，在我们的身体中有许多不同的组织具有专门的功能。这些组织彼此不同，因为组成特定组织的细胞中存在一组不同的蛋白质。原因是每个细胞中只有一组特定的基因是活跃的。基因表达是一个复杂的过程，因此它可以在几个层面上进行调节。首先，染色体DNA被转录成mRNA，这一步骤受到多种机制的调控。然后，mrna被加工并转运到细胞质中，在那里它们被翻译成蛋白质。蛋白质的积累也可以通过各种机制来调节。最近发现的调控层之一涉及短rna来调节mrna的翻译效率。这些短rna中的一组被称为微rna (mirna)，因为这些分子非常短，只有21-24个核苷酸。近十年的研究发现，mirna在正常发育中起着非常重要的作用。mirna是由一个更长的前体分子产生的，它被折叠成一个典型的茎环形状。这种形状以及前体和成熟mirna的其他一些特征，都与mirna的产生（生物发生）有关，可以通过计算机程序识别。开发了几个计算机程序，可以从从不同组织捕获的大量短序列中预测mirna。这些程序依赖于不同的参数，如杆和回路的长度以及许多其他参数，但这些参数的值是主观的，没有实验证明。自然，使用更严格参数的程序比使用更宽松参数的程序预测更少的mirna。到目前为止，已经应用了严格的参数来确保低假阳性预测率。但由于标准严格，可能会遗漏茎段稍短、环段稍大等mirna。mirna识别它们调节的mrna上的特定序列，在植物中，它们在该序列的第10位和第11位之间引起切割。除了生物发生特征外，还可以捕获裂解片段，提供功能信息。最近，我们开发了一个程序，可以比较所有裂解的mRNA片段和样本中发现的所有小rna。该程序在模式植物拟南芥（Arabidopsis thaliana）中发现了4000多个可能被3500个小rna切割的片段。数百种小rna表现出与mirna相似的特征，但略低于预测程序的严格标准。基于这些，我们假设计算机程序遗漏了许多mirna，这些mirna可以通过功能和生物发生数据一起识别。该提案的目的是通过开发一个新的程序来验证这一假设，该程序可以考虑生物发生和功能数据，并在模式物种拟南芥和作物物种番茄中识别许多新的mirna。在功能数据存在的情况下，我们建议使用稍微不那么严格的生物发生参数来自信地预测新的mirna。我们将从正常植物和含有低水平mirna或裂解mRNA片段的植物中生成特定的小RNA和裂解mRNA数据。这些将用于识别实验验证的参数。

How do sharks and roses develop from a single cell? This question has intrigued scientists for a long time because all plants and animals derive from a single cell, the fertilised egg. That single cell develops into an entire organism through many-many cell divisions but the genetic information does not change during those cell divisions. Therefore all our cells contain the same genetic information. However, there are many different tissues with specialised functions in our body. These tissues are different from each other because a different set of proteins are present in the cells that make up a certain tissue. The reason for this is that only a certain set of genes are active in each cell. Gene expression is a complex process therefore it can be regulated at several levels. First the chromosomal DNA is transcribed into mRNA and this step is regulated by various mechanisms. The mRNAs are then processed and translocated to the cytoplasm where they are translated into proteins. Accumulation of a protein can also be regulated by various mechanisms. One of the most recently discovered regulatory layers involves short RNAs to regulate the translation efficiency of mRNAs. A group of these short RNAs are called microRNAs (miRNAs) since these molecules are very short, only 21-24 nucleotides. Research in the last ten years found that miRNAs play a very important role in normal development.miRNAs are generated from a longer precursor molecule, which is folded into a characteristic stem-loop shape. This shape and a few other features of the precursor and mature miRNAs, which are all linked to how the miRNAs are produced (biogenesis) can be identified by computer programs. Several computer programs were developed that can predict miRNAs from a large number of short sequences captured from different tissues. These programs rely on different parameters such as the length of the stem and loop and many others but the value of these parameters are subjective and not experimentally proven. Naturally, programs using more stringent parameters predict fewer miRNAs than programs using more relaxed parameters. Until now, stringent parameters have been applied to ensure a low rate of false positive predictions. However, due to the strict criteria, miRNAs with a slightly shorter stem, bigger loop, etc. might have been missed. miRNAs recognise specific sequences on mRNAs they regulate and in plants they cause a cleavage between the 10th and 11th position within that sequence. The cleaved fragments can be captured providing functional information in addition to biogenesis features. Recently we developed a program that can compare all cleaved mRNA fragments to all small RNAs found in a sample. This program found more than 4000 fragments that were potentially cleaved by about 3500 small RNAs in the model plant Arabidopsis thaliana. Hundreds of those small RNAs showed similar features to miRNAs but marginally missed the stringent criteria of the prediction programs. Based on these we hypothesise that many miRNAs have been missed by computer programs and these could be identified using functional and biogenesis data together. The aim of the proposal is to test this hypothesis by developing a new program that can consider both biogenesis and functional data and identify many new miRNAs in the model species Arabidopsis and the crop species tomato. In the presence of functional data, we propose to use slightly less stringent biogenesis parameters to confidently predict new miRNAs. We will generate specific small RNA and cleaved mRNA data from normal plants and also from plants that contain reduced level of miRNAs or cleaved mRNA fragments. These will be used to identify experimentally validated parameters.