DNA sequence signals in the core promoter, such as the initiator (Inr), direct transcription initiation by RNA polymerase II. Here we show that the human Inr has the consensus of BBCA +1 BW at focused promoters in which transcription initiates at a single site or a narrow cluster of sites. The analysis of 7678 focused transcription start sites revealed 40% with a perfect match to the Inr and 16% with a single mismatch outside of the CA +1 core. TATA-like sequences are underrepresented in Inr promoters. This consensus is a key component of the DNA sequence rules that specify transcription initiation in humans.Supplemental material is available for this article.Received November 14, 2016; revised version accepted December 19, 2016. The multifarious signals that lead to the initiation of transcription ultimately converge at the core promoter, which is sometimes referred to as the gateway to transcription (for reviews, see Smale and Kadonaga 2003;Goodrich and Tjian 2010;Kadonaga 2012;Danino et al. 2015). The core promoter is the stretch of DNA-which typically is from about −40 to +40 nucleotides (nt) relative to the +1 transcription start site (TSS)-that directs the initiation of transcription. Core promoters are diverse in terms of their composition and function, and their activities are driven by the presence or absence of DNA sequence motifs such as the TATA box, initiator (Inr), TFIIB recognition elements (BRE u and BRE d ), polypyrimidine initiator (TCT), motif ten element (MTE), and downstream core promoter element (DPE). There are no universal core promoter motifs. Specific core promoter elements can be important for enhancer-promoter specificity (for example, see Butler and Kadonaga 2001; Juven-Gershon et al. 2008) as well as the regulation of gene networks (for example, see Juven-Gershon et al. 2008;Parry et al. 2010;Duttke et al. 2014;Wang et al. 2014).The long-term goal of this study is to gain a more specific understanding of the human core promoter. It has been estimated, for instance, that <25% of human core promoters contain the well-known TATA box or a TATA-like sequence (Gershenzon and Ioshikhes 2005;Carninci et al. 2006;Yang et al. 2007). In fact, it appears that the Inr is the most common core promoter element in humans. For example, ∼48%-49% of human promoters were found to have a sequence in the TSS region (from −5 to +6 relative to the +1 TSS) that is related to the 8-nt "cap signal" (i.e., Inr) position-weight matrix (based on 502 eukaryotic promoters) (Bucher 1990;Gershenzon and Ioshikhes 2005). In addition, it has been found that ∼46% of human promoters contain the YYA +1 NWYY Inr consensus within −80 to +80 nt relative to the TSS (Yang et al. 2007). These observations were interesting, but the precise sequence, abundance, and positioning of the human Inr remained to be determined.The Inr is an extensively studied core promoter element. The presence of a distinct sequence motif that encompasses the TSS was initially described by Corden et al. (1980), and the function of this sequence, w...

New technologies in genomics and proteomics have influenced the emergence of proteogenomics, a field at the confluence of genomics, transcriptomics, and proteomics. First generation proteogenomic toolkits employ peptide mass spectrometry to identify novel protein coding regions. We extend first generation proteogenomic tools to achieve greater accuracy and enable the analysis of large, complex genomes. We apply our pipeline to Zea mays, which has a genome comparable in size to human. Our pipeline begins with the comparison of mass spectra to a putative translation of the genome. We select novel peptides, those that match a region of the genome that was not previously known to be protein coding, for grouping into refinement events. We present a novel, probabilistic framework for evaluating the accuracy of each event. Our calculated event probability, or eventProb, considers the number of supporting peptides and spectra, and the quality of each supporting peptide-spectrum match. Our pipeline predicts 165 novel protein-coding genes and proposes updated models for 741 additional genes. Molecular & Cellular Proteomics 13: 10.1074/ mcp.M113.031260, 157-167, 2014.Accurate genome annotation, wherein the location and structure of all protein coding genes are identified, is critically important and yet it remains elusive for even the most extensively studied organisms. The wide availability of inexpensive next-generation sequencing technologies ensures that model organisms from all branches of the tree of life will continue to be sequenced at an ever increasing pace. However, the annotation pipelines are not able to keep up.Much recent focus on computational gene finding is on incorporating transcript evidence. As with genomic sequencing, availability of high-throughput technologies for transcript sequencing such as RNA-Seq (1) has dramatically changed the genome annotation landscape. Although RNA-Seq provides valuable evidence for genome annotation (2-5) it does not provide a comprehensive solution either. Increasing evidence suggests that a discrepancy exists between protein isoforms that are transcribed versus translated (6). Indeed in our own observation, we find evidence for genes in sampling proteins that are not visible at the transcript level. Moreover, the transcript evidence is confounded by prespliced messages, nontargeted expression noise, ncRNA, and lack of strand and frame information. All of these pose challenges for gene finding.Tandem mass spectrometry is a key technology for assaying the expressed proteome. In typical bottom-up workflows, enzymatically digested peptides are isolated via chromatography and then fragmented in the mass spectrometer. The collection of masses of peptide fragments (tandem mass spectrum) is used as a fingerprint for identification of expressed peptides.Historically, the genomics community has provided the annotations (aa sequences) and the proteomics community has focused on identifying peptides and proteins from this annotated list to assay for expression of proteins in specif...

The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to transcription initiation 1 - 5 , but the downstream core promoter in humans has been difficult to decipher 1 - 3 . Here, we analyze the human Pol II core promoter and use machine learning to generate predictive models for the downstream core promoter region (DPR) and the TATA box. We developed a method termed HARPE (high-throughput analysis of randomized promoter elements) to create hundreds of thousands of DPR (or TATA box) variants that are each of known transcriptional strength. We then analyzed the HARPE data by support vector regression (SVR) to provide comprehensive models for the sequence motifs, and found that the SVR-based approach is more effective than a consensus-based method for predicting transcriptional activity. These studies revealed that the DPR is a functionally important core promoter element that is widely used in human promoters. Importantly, there appears to be a duality between the DPR and TATA box, as many promoters contain one or the other element. More broadly, these findings show that functional DNA motifs can be identified by machine learning analysis of a comprehensive set of sequence variants.

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