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Bio. Spyder | Temp. O- Seq. Limitations of RNA- Seq. Gene expression studies can often benefit from testing large numbers of samples, obtaining repeatable as well as accurate results and from low sequencing costs/sample, and low analysis costs. Analysis platforms have evolved from outdated hybridization array based technologies to RNA- Seq, and quantitative real time reverse transcription PCR (q- RT- PCR) has evolved into highly multiplexed platforms. Though RNA- Seq has become a gold standard and can be used as a quantitative assay to determine relative transcript abundance, it is costly, onerous, and employs a time intensive process for assay design, running the assay and data analysis. Generating libraries for m. RNA sequencing is a difficult and often error prone process involving many steps with loss of sample at every step. The RNA must be extracted and reverse transcribed, then processed further to generate the sequencing library. The presence of high abundance RNAs (r. RNA, etc) requires additional steps to reduce background RNA and/or enrich for m. RNAs. Although these methods can help data quality, they add to the labor, cost and time required and deplete the amount of original sample, which is especially problematic when working with needle biopsies, rare transcripts or single cells. To address these issues, many resort to pre- amplification of the RNA as well as to deeper sequencing to increase the number of reads. This presents challenges to data analysis, reduces the number of samples that can be batched together in a single library, and increases both cost per sample and time. RNA- Seq is best used to identify new biomarkers or mutations, but is especially inefficient if gene markers have already been identified and consequently all the information of interest comes from this focused subset of genes. While q. RT- PCR based methods are perfectly acceptable when measuring low numbers of targets, they are impractical when large numbers of targets need to be analyzed with high throughput sample processing, and like RNA- Seq, require RNA to be extracted and reverse transcribed. Multiplexing the measurement of more than one gene at a time within the same PCR reaction requires extensive optimization, and is limited to at most 4 genes at a time in any given reaction. Microfluidic and microplate platforms are available that permit a sample to be split across multiple PCR reactions for many different genes, but when configured, for instance, to measure 9. Targeted sequencing methods have been developed that range from capturing a subset of targeted genes on an array and then releasing and sequencing these, to use of targeted PCR primers or reverse transcriptase primers to selectively amplify and process subsets of targeted genes. However, primer amplification- based methods of targeted sequencing have proven difficult to develop for a set of primers for each target, and as a result content is typically limited to 5. These approaches still require extraction and reverse transcription. These serious limitations of q. PCR, RNA- Seq, and targeted sequencing methods have driven the need for a higher throughput, higher content, simpler, and more sensitive targeted sequencing approach that is not limited in the number of genes that can be measured, or by the complexity of developing assays with different content, and which reduces the complexity of the transcriptome to short read sequences for each targeted gene. Figure 1: Temp. O- Seq Biochemistry. Introducing Tempo- Seq. To address these challenges, Bio. Spyder Technologies has developed a novel product for targeted sequencing called Temp. O- Seq™, designed to monitor hundreds to thousands of genes at once in high throughput from as little as 1. RNA (the amount from a single cell) without pre- amplification, to maximize utilization of precious or limited samples. Based on Bio. Spyder Technologies’ proprietary Templated Oligo Detection Assay, Temp. Development of Real-Time PCR Assays for the Quantitative Detection of Epstein-Barr Virus and Cytomegalovirus, Comparison of TaqMan Probes, and Molecular Beacons. The primers and fluorogenic probes that comprise the CPplc-IAC RTi-PCR system (CPplcF, CPplcR, CPplcP, and CPIACP) and the primers needed for the internal. Provides genome browser, gene sorter, blat search function, and publications. O- Seq can quantitate targeted transcripts in an easy to follow workflow that does not require dedicated equipment. It can be run in a standard PCR instrument or microplate incubator manually or using standard pipetting platforms. The assay is highly amenable to automation, enabling implementation on 9. Sample barcoding, together with sequencing of short templates to measure each gene, allows pooling up to 6,1. Assay content is flexible and customizable, from focused panels monitoring specific genes or cellular pathways up to the whole transcriptome, delivering unprecedented accuracy and sensitivity for low level inputs. Investigators can select focused content from an archive of detector oligos measuring the whole transcriptome, and add additional custom content such as the measurement of specific isotypes, fusions, or mutations. Together with robust probe design and simplified data analysis that eliminates the need for bioinformatics, Temp. O- Seq assays deliver an easy to use solution for customers doing expression profiling for any species. Temp. O- Seq is unique in its capacity to avoid RNA purification or reverse transcription, by targeting RNAs with detector oligos and removing excess probes and enzymatic inhibitors before the first enzymatic step. Correctly hybridized detector oligos are ligated, then amplified through primer landing sites that are shared among all probes (Figure 1). This approach permits high target multiplexing because although the central part of the ligated oligos contains diverse sequences, only two PCR primers are needed for any sample, eliminating the primer cross- hybridization and competition inherent in multiplex PCR. Dual sequence tags are incorporated during PCR, to identify up to 6,1. Key advantages to Temp. O- Seq include the capacity to definitively assign correctly ligated products to their RNA targets because the product is sequenced rather than read out on an array. Mis- ligated products can also be detected, unlike on arrays. As a result, and due to Bio. Spyder optimization efforts, the background reads for no- sample controls are nearly zero. Another advantage to Temp. O- Seq is that the assay only reports the intended targets. There is no need to eliminate globin or ribosomal RNAs. In addition, the requirement for ligation of two hybridized detector oligos means that the assay demonstrates excellent specificity, with 9. Consequently, the assay selectively measures and discriminates between all members of highly homologous gene families, such as the CYP4. The assay does not require dedicated machinery, and is amenable to automation for high throughput applications. Figure 2: Typical Temp. O- Seq Performance. The assay demonstrates excellent reproducibility (Figure 2), with log. R2 values routinely exceeding 0. The data shown are raw reads, no normalization, for triplicates of total RNA preps from two cell lines (left and center panels). Comparing these cell lines shows dramatic differences in expression, as expected (right panel). Finally, because the sequencing reports the number of ligated probes, the data analysis is simple. Rather than aligning the reads to the genome, Temp. O- Seq reads are compared to a look up table of ligated detector oligos input to the assay, a task that can be completed on a standard PC within minutes. Bio. Spyder has developed a data analysis pipeline for users to convert FASTQ files to data tables, with assay quality metrics reported as well, eliminating the need for investigators to have bioinformatics support to perform analysis and generate tables of gene identity versus abundance, or to normalize data between replicates and treatments. Figure 3: Expression Levels for ~9. Probes in Triplicate Temp. O- Seq Assays. Detector Oligo Design. Temp. O- Seq’s whole trancriptome probe design pipeline creates detector oligos by maximizing the number of isoforms targeted per gene, selecting for the optimal GC content/thermodynamic properties, selecting against hairpins, avoiding homopolymer stretches and repetitive elements, and avoiding detector oligos pairs that overlap SNPs. Additionally, the Temp. O- Seq detector oligo design pipeline designs detector oligo pairs with maximum ligation efficiency and specificity. The design pipeline takes advantage of the ligation properties of the Temp. O- Seq assay to design probes that can specifically detect all genes in the transcriptome with the ability to distinguish a 1 base- pair mismatch with 9. Novel computational methods for increasing PCR primer design effectiveness in directed sequencing. Computational PCR primer design. Software. In the method described here, we constrain the PCR primer design software to generate a set of primers with a high likelihood of success using one of two standardized amplification protocols. The high- throughput computational PCR primer design pipeline is one of the first computational stages we employ as part of our directed sequencing pipeline (Figure 1). An overview of the JCVI High- throughput Directed Sequencing Pipeline. The computational PCR primer design software is available on the Source. Forge. net JCVI Primer Designer website [1. The tar file of the latest release is available by navigating through the 'Download' tab. It can be installed and run on any computer with a Unix operating system. Inputs. Target regions are specified by the investigator through a variety of identifiers. By using the feature rich Ensembl Perl Application Programming Interface (API) [1. Gene, Transcript, HUGO, Exon, and db. SNP. Since the reference genome assembly and annotation may be updated, using identifiers allows us to track features of interest even if their absolute genomic coordinates have changed over time. In addition, by using these identifiers as anchors, we are able to augment target regions with a specified number of flanking bases upstream and downstream of the region, and, if applicable, around exons. The specification of flanking bases, which essentially provides padding around features, implicitly allows the ability to target promoter regions, splice sites, and introns, relative to exons. Since there is also an interest in non- coding regions, we support arbitrary genomic regions as input based on chromosome coordinates. Additionally, the investigator has the option of including evolutionary conserved regions (ECRs) in their target region. We define ECRs as overlapping conserved regions between the human, mouse, and rat genomes established by a set of alignment criteria (> 1. The ECRs for the individual species are obtained dynamically from the Dcode ECR Browser website [1. Outputs. The output of the primer design pipeline is a set of primer pair sequences and their theoretical amplicons based on a reference sequence. In addition, we output a summary of the critique results, such as the primer and amplicon melting temperatures and information about trace phase shifting events. The primers can be optionally 5' tailed with M1. A GFF File [1. 4] is produced to allow the investigator to review the spatial organization of the amplicons in juxtaposition with sequence annotation. The GFF file also highlights target regions where primer pairs could not be designed. This allows the investigator to decide whether to proceed with sequencing, despite having areas that lack coverage, or to initiate a more customized primer design, targeting the uncovered regions. Figure 2 shows an example of a rendered GFF file, in the form of an automatically generated Adobe PDF file, incorporating the Ensembl annotation and designed amplicons. In addition to the visual summary, a statistical overview is also generated which reports the number of amplicons that were designed and their average, minimum, maximum and standard deviation of lengths. For high- throughput primer designs, we have project level tools which summarize the total and percentage of targeted base pairs that were covered. Sample output from a primer design run displaying amplicons in relation to genomic features. Algorithm. The primer design pipeline is composed of two key software components: the Coverage Manager (CM) and the Primer Critiquor (PC). The Coverage Manager (CM) is responsible for generating a dynamic amplicon tiling across the supplied target regions. Its parameters include the target, minimum, and maximum amplicon size, the minimum number of overlapping base pairs required between amplicons, and the depth of redundant amplicon coverage required. Overlap between amplicons is typically warranted when using Sanger sequencing because approximately 5. As a result, an overlap of at least 1. The CM employs Primer. The CM will then invoke the Primer Critiquor (PC) to determine whether or not to accept a primer pair candidate. If multiple target regions are proximally located, the CM will subsume the regions with one amplicon. If the target region is smaller than an amplicon, the CM attempts to center that region within the amplicon. Otherwise, the CM will attempt to dynamically generate a tiling from the 5' to 3' end across the target region. After the first amplicon has been selected, every proceeding amplicon will depend on the length and position of the prior upstream amplicon and the required minimum amplicon overlap. In order to assay targeted regions, non- uniform tiling can be justified because of our ability to select primers which will be successful. This greedy method for amplicon tiling is more efficient than a strictly uniformly spaced tiling methodology [5,7,8] because we can maximize coverage while minimizing redundancy. Target regions are left uncovered by amplicons only if all primer pair candidates have been rejected in that region. The Primer Critiquor (PC) is responsible for determining whether a primer pair passes or fails our selection criteria. The parameters for most of the criteria have been determined based on prior directed sequencing experiments, providing us the advantage of avoiding a "fuzzy" logic based approach to evaluating the potential success of primer pairs [1]. The PC was designed to have each criterion specified in a modular fashion. In the event that a new failure pattern is discovered, a new criterion can easily be incorporated as an additional module. The PC may be invoked iteratively when the CM needs to test primer pairs during a primer design run, or it may be called upon independently to evaluate an arbitrary set of primer pairs generated by methods that are external to the current pipeline. The following criteria are evaluated by the PC to determine whether to accept a given primer pair: Alternative amplification detection. Alternative amplification occurs during PCR when primers bind to and amplify non- targeted regions. This is especially problematic when using genomic DNA samples. To check for alternative amplification events, we use BLASTN [1. The BLAST alignments are then filtered and accepted as hits if they meet either of the following two criteria: 1) > 8. The detection of alternative amplification depends on how well characterized the reference genome is or how similar the reference genome is to the target organism. If the length of a predicted alternative amplicon exceeds a parameterized threshold, its amplification is not considered viable. We determined this threshold to be 1. PCR gel results against the computationally predicted alternative products. We observed multiple instances which indicated that computationally predicted alternative products were false positives when their lengths exceeded this 1. We consider this to be a conservative threshold because we also observed instances where computationally predicted alternative amplicons shorter than 1. In these cases, the primer binding for the targeted amplicon was more thermodynamically stable and, therefore, out- competed the alternative amplicons. A primer pair is rejected if that pair yields at least one viable alternative amplification product. Due to the interest in diseases associated with non- coding regions, such as introns, regulatory regions, as well as intergenic regions, it is important to support the ability to target genomic regions rich in repetitive elements. We do not mask repeat regions on the reference genome before searching for primer binding sites, since it is possible for a primer pair to be specific even when one or both of the primers binds to a repetitive region. We employ the following heuristic to expedite alternative amplification detection. If the primer pair can computationally amplify a product in the repeat library, then the primer pair is immediately failed. If either primer in the pair can bind to a repetitive element shorter than 5. Primers that bind to longer repeats are passed on to the full genome search. This heuristic allows primers to flank short repeats while forcing primer design to tackle long repeats where multiple tiled amplicons are necessary. Trace phase shifting event detection. San Diego Venues, Best Venues for Perfect Setting. Phone : (6. 19) 2. Stunning Historical Architecture, Picturesque Terraces, Lush Garden Settings, And Elegant Ballrooms That Can Accommodate Groups Of Various Sizes And Cultures. Venues Include –Reuben H. Fleet Science Center, Memorial Building Auditorium, The San Diego Museum of Art, San Diego Natural History Museum, San Diego Museum of Man, , Redwood Circle, Recital Hall, Museum of Photographic Arts, Casa del Prado Majorca Room, Casa del Prado Courtyard. 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