What do promoter sequences do

Since most bioinformatics problems focused on sequencing data, their features could be extracted by using our combination different levels of FastText N-grams. They then be fed into a supervised learning to perform the prediction or classification e. It could also provide a new approach for the previous works that only used one level of FastText Le, ; Le et al. A combination of more levels could be a solution for boosting their predictive performances.

Furthermore, since a lot of previous works on promoter classification extracted features by using PseKNC [such as Liu et al. Publicly available datasets were analyzed in this study. NL and EY conceived the ideas and designed study. NL conducted the experiments and analyzed the results.

All authors read and approved the final version of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Asgari, E. Continuous distributed representation of biological sequences for deep proteomics and genomics.

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Identifying antimicrobial peptides using word embedding with deep recurrent neural networks. Bioinformatics 35, — Structural properties of gene promoters highlight more than two phenotypes of diabetes. Ioshikhes, I. Large-scale human promoter mapping using CpG islands. Keller, J. A fuzzy k-nearest neighbor algorithm. IEEE Trans. Man Cybern. Knudsen, S. Bioinformatics 15, — Le, N. Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles.

Methods Programs Biomed. PeerJ Comp. Li, Q. Lin, H. Identifying sigma70 promoters with novel pseudo nucleotide composition. Liu, B. Bioinformatics 34, 33— Nguyen, T. Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network. Ohler, U. Interpolated markov chains for eukaryotic promoter recognition. A novel methodology on distributed representations of proteins using their interacting ligands.

Bioinformatics 34, i—i Ponger, L. C, and Mouchiroud, D. CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences. Bioinformatics 18, — A promoter is a sequence of DNA needed to turn a gene on or off. The process of transcription is initiated at the promoter. Usually found near the beginning of a gene, the promoter has a binding site for the enzyme used to make a messenger RNA mRNA molecule.

Adam optimizer Kingma and Ba, is used for updating the parameters with a learning rate of 0. The batch size is set to 32 and the number of epochs is set to Early stopping is applied based on validation loss. In this work, we use the widely adopted evaluation metrics for evaluating the performance of the proposed models.

These metrics are precision, recall, and Matthew correlation coefficient MCC , and they are defined as follows:. Where TP is true positive and represents correctly identified promoter sequences, TN is true negative and represents correctly rejected promoter sequences, FP is false positive and represents incorrectly identified promoter sequences, and FN is false negative and represents incorrectly rejected promoter sequences. When analyzing the previously published works for promoter sequences identification we noticed that the performance of those works greatly depends on the way of preparing the negative dataset.

They performed very well on the datasets that they have prepared, however, they have a high false positive ratio when evaluated on a more challenging dataset that includes non-prompter sequences having common motifs with promoter sequences. For instance, in case of the TATA promoter dataset, the randomly generated sequences will not have TATA motif at the position and —25 bp which in turn makes the task of classification easier. In other words, their classifier depended on the presence of TATA motif to identify the promoter sequence and as a result, it was easy to achieve high performance on the datasets they have prepared.

However, their models failed dramatically when dealing with negative sequences that contained TATA motif hard examples. The precision dropped as the false positive rate increased. Simply, they classified these sequences as positive promoter sequences.

A similar analysis is valid for the other promoter motifs. Therefore, the main purpose of our work is not only achieving high performance on a specific dataset but also enhancing the model ability on generalizing well by training on a challenging dataset. To more illustrate this point, we train and test our model on the human and mouse TATA promoter datasets with different methods of negative sets preparation.

The first experiment is performed using randomly sampled negative sequences from non-coding regions of the genome i. These high results are expected, but the question is whether this model can maintain the same performance when evaluated on a dataset that has hard examples. The answer, based on analyzing the prior models, is no.

The second experiment is performed using our proposed method for preparing the dataset as explained in section 2. This ensures that our model learns more complex features rather than learning only the presence or absence of TATA-box. Figure 5. Over the past years, plenty of promoter region prediction tools have been proposed Hutchinson, ; Scherf et al.

However, some of these tools are not publically available for testing and some of them require more information besides the raw genomic sequences.

In this study, we compare the performance of our proposed models with the current state-of-the-art work, CNNProm, which was proposed by Umarov and Solovyev as shown in Table 2. On the other hand, our models are able to deal with these cases more successfully and false positive rate is lower compared with CNNProm.

For further analyses, we study the effect of alternating nucleotides at each position on the output score. We focus on the region —40 and 10 bp as it hosts the most important part of the promoter sequence. Blue color represents a drop in the output score due to mutation while the red color represents the increment of the score due to mutation.

We notice that altering the nucleotides to C or G in the region —30 and —25 bp reduces the output score significantly. This region is TATA-box which is a very important functional motif in the promoter sequence. Thus, our model is successfully able to find the importance of this region. In the rest of the positions, C and G nucleotides are more preferable than A and T, especially in case of the mouse. This can be explained by the fact that the promoter region has more C and G nucleotides than A and T Shi and Zhou, Figure 6.

Figure 7. Accurate prediction of promoter sequences is essential for understanding the underlying mechanism of the gene regulation process. In this work, we were particularly interested in constructing a hard negative set that drives the models toward exploring the sequence for deep and relevant features instead of only distinguishing the promoter and non-promoter sequences based on the existence of some functional motifs.

The main benefits of using DeePromoter is that it significantly reduces the number of false positive predictions while achieving high accuracy on challenging datasets. DeePromoter outperformed the previous method not only in the performance but also in overcoming the issue of high false positive predictions. It is projected that this framework might be helpful in drug-related applications and academia. MO and ZL prepared the dataset, conceived the algorithm, and carried out the experiment and analysis.

All authors discussed the results and contributed to the final manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Alipanahi, B. Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Angermueller, C. Deepcpg: accurate prediction of single-cell dna methylation states using deep learning. Genome Biol. Baker, T.

Benjamin-Cummings Publishing Company. Google Scholar. Behjati, S. What is next generation sequencing? Childhood Educ. Bharanikumar, R. PeerJ 6:e Chollet, F. Astrophysics Source Code Library. Dahl, J. A rapid micro chromatin immunoprecipitation assay chip.

Davuluri, R. Computational identification of promoters and first exons in the human genome. Down, T. Computational detection and location of transcription start sites in mammalian genomic dna. Genome Res. Dreos, R. Epd and epdnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Res. The DNA molecule itself can also be modified. This occurs within very specific regions called CpG islands. These are stretches with a high frequency of cytosine and guanine dinucleotide DNA pairs CG found in the promoter regions of genes.

When this configuration exists, the cytosine member of the pair can be methylated a methyl group is added. This modification changes how the DNA interacts with proteins, including the histone proteins that control access to the region. Highly-methylated hypermethylated DNA regions with deacetylated histones are tightly coiled and transcriptionally inactive. These changes to DNA are inherited from parent to offspring, such that while the DNA sequence is not altered, the pattern of gene expression is passed to the next generation.

This type of gene regulation is called epigenetic regulation. Instead, these changes are temporary although they often persist through multiple rounds of cell division and alter the chromosomal structure open or closed as needed.

A gene can be turned on or off depending upon the location and modifications to the histone proteins and DNA. If a gene is to be transcribed, the histone proteins and DNA are modified surrounding the chromosomal region encoding that gene.

This opens the chromosomal region to allow access for RNA polymerase and other proteins, called transcription factors, to bind to the promoter region, located just upstream of the gene, and initiate transcription. If a gene is to remain turned off, or silenced, the histone proteins and DNA have different modifications that signal a closed chromosomal configuration.

In this closed configuration, the RNA polymerase and transcription factors do not have access to the DNA and transcription cannot occur. RNA splicing allows for the production of multiple protein isoforms from a single gene by removing introns and combining different exons. Gene expression is the process that transfers genetic information from a gene made of DNA to a functional gene product made of RNA or protein.

In order to ensure that the proper products are produced, gene expression is regulated at many different stages during and in between transcription and translation.

In eukaryotes, the gene contains extra sequences that do not code for protein. These pre-mRNA transcripts often contain regions, called introns, that are intervening sequences which must be removed prior to translation by the process of splicing. The regions of RNA that code for protein are called exons. Splicing can be regulated so that different mRNAs can contain or lack exons, in a process called alternative splicing. Alternative splicing allows more than one protein to be produced from a gene and is an important regulatory step in determining which functional proteins are produced from gene expression.

Thus, splicing is the first stage of post-transcriptional control. Alternative Splicing : There are five basic modes of alternative splicing. Alternative splicing is a process that occurs during gene expression and allows for the production of multiple proteins protein isoforms from a single gene coding.

Alternative splicing can occur due to the different ways in which an exon can be excluded from or included in the messenger RNA. This results in what is called alternative splicing. The pattern of splicing and production of alternatively-spliced messenger RNA is controlled by the binding of regulatory proteins trans-acting proteins that contain the genes to cis-acting sites that are found on the pre-RNA.

Some of these regulatory proteins include splicing activators proteins that promote certain splicing sites and splicing repressors proteins that reduce the use of certain sites. Some common splicing repressors include: heterogeneous nuclear ribonucleoprotein hnRNP and polypyrimidine tract binding protein PTB. Proteins that are translated from alternatively-spliced messenger RNAs differ in the sequence of their amino acids which results in altered function of the protein.

This is one reason why the human genome can encode a wide diversity of proteins. Alternative splicing is a common process that occurs in eukaryotes; most of the multi-exonic genes in humans are spliced alternatively.

Unfortunately, abnormal variations in splicing are also the reason why there are many genetic diseases and disorders. Mechanism of Splicing : Alternative splicing can result in protein isoforms. The splicing of messenger RNA is accomplished and catalyzed by a macro-molecule complex known as the spliceosome. Interactions between these sub-units and the small nuclear ribonucleoproteins snRNP found in the spliceosome create a spliceosome A complex which helps determine which introns to leave out and which exons to keep and bind together.

Once the introns are cleaved and removed, the exons are joined together by a phosphodiester bond. As noted above, splicing is regulated by repressor proteins and activator proteins, which are are also known as trans-acting proteins.

Equally as important are the silencers and enhancers that are found on the messenger RNAs, also known as cis-acting sites.

These regulatory functions work together in order to create splicing code that determines alternative splicing. Like transcription, translation is controlled by proteins that bind and initiate the process. In translation, before protein synthesis can begin, ribosome assembly has to be completed.

This is a multi-step process. In ribosome assembly, the large and small ribosomal subunits and an initiator tRNA tRNA i containing the first amino acid of the final polypeptide chain all come together at the translation start codon on an mRNA to allow translation to begin. First, the small ribosomal subunit binds to the tRNA i which carries methionine in eukaryotes and archaea and carries N-formyl-methionine in bacteria. Because the tRNA i is carrying an amino acid, it is said to be charged.

Next, the small ribosomal subunit with the charged tRNA i still bound scans along the mRNA strand until it reaches the start codon AUG, which indicates where translation will begin.