Statistical tools for transgene copy number estimation based on real-time PCR

Background As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Results Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. Conclusion These statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification

Incomplete homogenization of 18 S ribosomal DNA coding regions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>As a result of concerted evolution, coding regions of ribosomal DNA sequences are highly conserved within species and variation is generally thought to be limited to a few nucleotides. However, rDNA sequence variation has not been systematically examined in plant genomes, including that of the model plant <it>Arabidopsis thaliana </it>whose genome was the first to be sequenced.</p> <p>Findings</p> <p>Both genomic and transcribed 18 S sequences were sampled and revealed that most deviation from the consensus sequence was limited to single nucleotide substitutions except for a variant with a 270 bp deletion from position 456 to 725 in <it>Arabidopsis </it>numbering. The deletion maps to the functionally important and highly conserved 530 loop or helix18 in the structure of <it>E. coli </it>16 S. The expression of the deletion variant is tightly controlled during developmental growth stages. Transcripts were not detectable in young seedlings but could be amplified from RNA extracts of mature leaves, stems, flowers and roots of <it>Arabidopsis thaliana </it>ecotype Columbia. We also show polymorphism for the deletion variant among four <it>Arabidopsis </it>ecotypes examined.</p> <p>Conclusion</p> <p>Despite a strong purifying selection that might be expected against functionally impaired rDNAs, the newly identified variant is maintained in the <it>Arabidopsis </it>genome. The expression of the variant and the polymorphism displayed by <it>Arabidopsis </it>ecotypes suggest a transition state in concerted evolution.</p

Diversifying the genomic data science research community

Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support