我们要承认美国农业部农业研究服务处和国家科学基金会（IOS 1025881和IOS 1361554）的资助。感谢R. Caspers进行温室维护和植物保养。我们还感谢明尼苏达大学基因组学中心，其中进行了Illumina图书馆的制备和测序。我们也感谢期刊编辑和四位匿名审稿人的意见，进一步加强了我们的稿件。我们也感谢经合组织与SK的合作，将这些协议项目与日本的同事整合起来。

<ol>
	<li>Liberatore, K. L., Dukowic-Schulze, S., Miller, M. E., Chen, C., Kianian, S. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+mitochondria+in+plant+development+and+stress+tolerance.">The role of mitochondria in plant development and stress tolerance.</a> Free Radic Biol Med. 100, 238-256 (2016).</li><li>Samaniego Castruita, J. A., Zepeda Mendoza, M. L., Barnett, R., Wales, N., Gilbert, M. 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作者宣称他们没有竞争的利益。 本出版物中提及的商品名称或商业产品仅用于提供具体信息，并不意味着美国农业部的建议或认可。 USDA是一个平等的机会提供者和雇主。

迄今为止，大多数细胞器测序研究集中于传统的DC方法，以丰富特定的DNA。已经描述了从不同植物中分离细胞器的方法，包括苔藓40 ;单子叶植物如小麦15和燕麦11 ;和双子叶植物如拟南芥11 ，向日葵17和油菜籽14 。大多数协议集中在叶组织<s...

<html>基因组测序是解析重要植物性状的潜在遗传基础的有力工具。大多数基因组测序研究集中于核基因组内容，因为大多数基因位于细胞核中。然而，细胞器基因组，包括线粒体（跨真核生物）和质（植物;在特殊形式，叶绿体，工作在光合作用）促进生物发育，应激反应和整体健康至关重要的1显著的遗传信息。细胞器基因组通常包括在用于核基因组测序的总DNA的提取，虽然方法，以减少前，DNA提取的细胞器号码也采用2。许多研究已经使用测序结果从总的gDNA提取组装细胞器基因组3，4，5，外部参照"&gt; 6,7。然而，当研究的目标是把重点放在细胞器基因组，使用总的gDNA增加了测序成本，因为很多的读取，但‘丢失’的核的DNA序列，特别是在植物具有大核基因组此外，由于重复和传输细胞器序列的进核基因组和细胞器之间，解决测序的正确的映射位置读取到适当的基因组是生物信息学有挑战性2,8。细胞器基因组的从核基因组的纯化是一个减少这些问题的策略，进一步的生物信息学策略可用于分离映射到线粒体和叶绿体之间同源区域的读数。 虽然来自许多植物物种的细胞器基因组已被测序，但是对于细胞器基因组多样性的广度几乎不了解可用于野生种群或栽培育种池。还知道细胞器基因组是由于重复序列9之间的重组而经历显着的结构重排的动态分子。此外，每个细胞器中含有多个拷贝的细胞器基因组，并且每个细胞内都含有多个细胞器。并非所有这些基因组的拷贝都是相同的，这被称为异质性。与"主圈"的规范图相比，现在越来越多的证据表明，细胞基因组结构更复杂，包括亚基因组圆，线性染色体，线性连接子和支链结构10 。植物细胞器基因组的组装由于其相对较大的尺寸和大量的反向和直接重复而进一步复杂化。 用于细胞器分离，DNA纯化和随后基因组的传统方案<html> È测序往往笨重且需要大体积的组织输入的，具有几克到几百个向上必要为起点11，12，13，14，15，16，17克幼叶组织的组成。当组织受限时，这使得细胞器基因组测序不可及。在某些情况下，种子数量有限，例如当需要在代际基础上进行序列或在必须通过杂交维持的雄性不育系中时。在这些情况下，可以纯化细胞器DNA，然后进行全基因组扩增。然而，全基因组扩增可以引入显着的测序偏差，这在评估结构变异，亚基因组结构和异质性水平时是一个特别的问题&gt; 18。用于短读序列技术的图书馆准备工作的最新进展克服了低输入障碍，以避免全基因组扩增。例如，Illumina Nextera XT库制备试剂盒允许使用少至1ng的DNA作为输入19 。然而，用于长读取测序应用（如PacBio或Oxford Nanopore测序技术）的标准库制备仍然需要相对较高量的输入DNA，这可能对组织基因组测序构成挑战。最近，新的用户制作，长读测序的协议已被开发，以减少输入量和帮助促进在获得的DNA微克，批量样品的基因组测序是困难的20，21。然而，获得高分子量纯的细胞器级分以进入这些文库制剂仍是一个挑战。 我们寻求o比较和优化不需要全基因组扩增的适用于NGS的细胞器DNA富集和分离方法。具体来说，我们的目标是确定从有限起始材料（如叶子子样品）中丰富高分子量细胞器DNA的最佳实践。这项工作提供了丰富细胞DNA的方法的比较分析:（1）修改的传统差速离心方案与（2）基于使用市售DNA CpG-甲基结合结构域蛋白质下拉法的DNA分级方案22施用于植物组织23 。我们建议从小麦叶组织中分离细胞叶DNA的最佳实践，这可能容易地扩展到其他植物和组织类型。

<table border="0" cellpadding="0" cellspacing="0" width="685">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">2-mercaptoethanol (beta-mercaptoethanol; BME)</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td style="width:119px;">M3148-100ml</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">2-propanol (Isopropyl alcohol/isopropanol), bioreagent</td>
			<td>Sigma Aldrich</td>
			<td>I9516</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">agarose, Bio-Rad Cetified Megabase agarose</td>
			<td>Bio-Rad</td>
			<td>1613108</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">analytical balance</td>
			<td>Mettler Toledo</td>
			<td>AB54-S</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">balance</td>
			<td>Mettler Toledo</td>
			<td>PB1502-S</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">bovine serum albumin (BSA)</td>
			<td>Sigma Aldrich</td>
			<td>B4287-25G</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ceramic grinding cylinders, 3/8in x 7/8in</td>
			<td>SPEX SamplePrep</td>
			<td>2183</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Cryogenic Blocks compatible with tissue homogenizer for holding 50 ml tubes</td>
			<td>SPEX SamplePrep</td>
			<td>2664</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DNaseI</td>
			<td>Sigma</td>
			<td>DN25</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ethanol, absolute</td>
			<td>Decon Laboratories</td>
			<td>2716</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ethylenediamine Tetraacetic Acid (EDTA), 0.5M Solution, pH8.0</td>
			<td>Fisher</td>
			<td>BP2482-500</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">gel imaging system</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">gel stain</td>
			<td></td>
			<td></td>
			<td style="width:167px;">Such as GelRed or Ethidium Bromide</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">grinding pestle, wide tip for 2 ml conical tubes</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Guanidine-HCl, 8M solution</td>
			<td>ThermoFisher</td>
			<td>24115</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">LightCycler 480 SYBR Green I Master</td>
			<td>Roche</td>
			<td>4707516001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">liquid nitrogen</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Lysing enzymes from Trichoderma harzianum</td>
			<td>Sigma</td>
			<td>L1412</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Magnesium Chloride</td>
			<td>G Bioscience</td>
			<td>24115</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">magnetic rack</td>
			<td>ThermoFisher</td>
			<td>A13346</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">microcentrifuge tubes, LoBind 1.5 ml&#160;</td>
			<td>Eppendorf</td>
			<td>22431021</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">microcentrifuge tubes, standard nuclease-free 1.5 ml</td>
			<td>Eppendorf</td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="120">
			<td height="120" style="height:120px;width:216px;">microcentrifuge, refrigerated</td>
			<td>Sorvall&#160;</td>
			<td>Legend X1R</td>
			<td style="width:167px;">Or equivalent product, must be capable of reaching at least 18,000 x g with rotors for 50 ml tubes, Oak Ridge tubes, and 1.5 ml tubes</td>
		</tr>
		<tr height="120">
			<td height="120" style="height:120px;width:216px;">microcentrifuge, room temperature</td>
			<td>Eppendorf</td>
			<td>5424</td>
			<td style="width:167px;">Or equivalent product, must be capable of reaching at least 18,000 x g with rotor for 1.5 ml and 2 ml microcentrifuge tubes</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Microcon DNA Fast Flow Centrifugal Filter Units</td>
			<td>EMD Millipore</td>
			<td>MRCFOR100</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Miracloth, 1 square per sample cut to fit funnel</td>
			<td>EMD Millipore</td>
			<td>475855</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">NEBNext Microbiome DNA Enrichment Kit</td>
			<td>New England Biolabs</td>
			<td>E2612L</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">parafilm</td>
			<td>Parafilm M</td>
			<td>PM992</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">plastic pots and trays</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">polyvinylpyrrolidone (PVP)</td>
			<td>Fisher</td>
			<td>BP431-100</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Proteinase K</td>
			<td>Qiagen</td>
			<td>19131</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Pulsed-Field Gel Electrophoresis rig (e.g. CHEF DR III)</td>
			<td>Bio-Rad</td>
			<td>1703697</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">purification beads, Agencourt AMpureXP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">QIAamp DNA Mini Kit</td>
			<td>Qiagen</td>
			<td>51304</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Qiagen 20/g Genomic Tip DNA Extraction Kit</td>
			<td>Qiagen</td>
			<td>10223</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qiagen Buffer EB (elution buffer)</td>
			<td>Qiagen</td>
			<td>19086</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Qiagen DNA Extraction Buffer Set</td>
			<td>Qiagen</td>
			<td>19060</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">QiaRack</td>
			<td>Qiagen</td>
			<td>19015</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">qPCR machine (e.g. Roche Light Cycler 480)</td>
			<td>Roche</td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">qPCR plate sealing film</td>
			<td>Roche</td>
			<td>4729757001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">qPCR plate, 96 well plate</td>
			<td>Roche</td>
			<td>4729692001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit&#160;assay tubes</td>
			<td>Life Technologies</td>
			<td>Q32856</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit Broad Spectrum assay kit</td>
			<td>Life Technologies</td>
			<td>Q32850</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit High Sensitivity assay kit</td>
			<td>Life Technologies</td>
			<td>Q32851</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RNaseA</td>
			<td>Qiagen</td>
			<td>19101</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Serological pipettes (20 ml) and pipet-aid</td>
			<td>Fisher</td>
			<td>13-678-11</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Small funnels, 1 per sample</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Sodium Chloride</td>
			<td>Ambion</td>
			<td>AM9759</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Soft paintbrush, 2 per sample</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="260">
			<td height="260" style="height:260px;width:216px;">SPEX SamplePrep 2010 Geno/Grinder or another type of tissue homogenizer</td>
			<td>SPEX SamplePrep</td>
			<td></td>
			<td style="width:167px;">Or another comparable tissue homogenizer. If you do not have access to a tissue homogenizer, then grinding in a pre-chilled mortar and pestle will suffice (see protocol for details). However, a homogenizer will give more consistent results and total homogenization time is reduced.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Sucrose</td>
			<td>Omnipure</td>
			<td>8550</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">TBE</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">thermomixer</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Tris</td>
			<td>Sigma</td>
			<td>T2819-100ml</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Triton X-100</td>
			<td>Promega</td>
			<td>H5142</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">tube rotater</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">tubes, 50 mL conical polypropylene</td>
			<td>Corning</td>
			<td>352070</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">tubes, 50 ml high-speed polypropylene&#160;</td>
			<td>ThermoScientific/Nalgene</td>
			<td>3119-0050</td>
			<td style="width:167px;">e.g. Nalgene Oakridge tubes or equivalent</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">vermiculite</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">water bath</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">water, sterile and certified Nuclease-free&#160;</td>
			<td>Fisher</td>
			<td>1481</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">water, sterile milliQ</td>
			<td></td>
			<td></td>
			<td style="width:167px;"></td>
		</tr>
	</tbody>
</table>

optimization and comparative analysis of plant organellar dna enrichment methods suitable for next-generation sequencing

植物细胞器基因组包含大的重复元件，其可能经历配对或重组以形成复合结构和/或亚基因组片段。细胞器基因组也存在于给定细胞或组织类型（异质性）内的混合物中，并且丰富的亚型可能在整个发育过程中或当处于应力（亚化学计量转移）时）发生变化。需要下一代测序（NGS）技术来更深入地了解细胞器基因组结构和功能。传统的测序研究使用几种方法来获得细胞器DNA:（1）如果使用大量的起始组织，将其均质化并进行差速离心和/或梯度纯化。 （2）如果使用较少量的组织（ 即如果种子，材料或空间有限），则按照（1）所述进行相同的处理，然后进行全基因组扩增以获得足够的DNA。 （3）生物信息学分析可用于分析使用总基因组DNA并解析出细胞器读数。所有这些方法都有固有的挑战和权衡。在（1）中，可能难以获得如此大量的起始组织;在（2）中，全基因组扩增可能引入测序偏倚;在（3）中，核和细胞器基因组之间的同源性可能会干扰组装和分析。在具有大核基因组的植物中，富集细菌DNA以降低生物信息学分析的测序成本和序列复杂度是有利的。在这里，我们将传统的差速离心法与第四种方法（适应性CpG-甲基下拉法）进行比较，将总基因组DNA分离成核和细胞器级分。两种方法为NGS产生足够的DNA，对于细胞器序列高度富集的DNA，尽管在线粒体和叶绿体中具有不同的比例。我们介绍了这些方法对小麦叶组织的优化，并讨论了主要优点和d在样本输入，协议易用性和下游应用的上下文中的每种方法的优点。

本手册中提出的方案描述了从植物组织中丰富细胞器DNA的两种不同的方法。这里提出的条件反映了小麦组织的优化。 图1中描述了协议，组织输入和DNA输出中的关键步骤的比较。我们测试的DC协议的步骤遵循与先前描述的相似的条件（ 图1A ）。收获的组织必须新鲜处理，并进行差速离心和/或梯度以分离完整的细胞器。在细胞器?...

Watch this Scientific Journal Video about Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing at JoVE.com

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

提出了两种植物细菌DNA富集方法的比较和优化:传统差速离心和基于甲基化状态的总gDNA分馏。我们评估产生的DNA数量和质量，证明在短读下一代测序中的性能，并讨论长读单分子测序中使用的潜力。

适用于下一代测序的植物细菌DNA富集方法的优化与比较分析

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic ...

optimization-comparative-analysis-plant-organellar-dna-enrichment

Research

JoVE Journal

Genetics

12.8K 次观看.  United States Department of Agriculture-Agricultural Research Service. 提出了两种植物细菌DNA富集方法的比较和优化:传统差速离心和基于甲基化状态的总gDNA分馏。我们评估产生的DNA数量和质量，证明在短读下一代测序中的性能，并讨论长读单分子测序中使用的潜力。 

视频: Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

Methyl-binding DNA capture Sequencing for Patient Tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

为患者组织甲基结合DNA序列捕获

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

科研

遗传学

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

利用CyVerse资源<em&gt; De Novo</em&gt;不合格（非模型）生物的比较转录组学

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time PCR (RT-qPCR). It provides accurate, sensitive, reliable, and reproducible results. Several factors can affect the sensitivity and specificity of RT-qPCR. Residual genomic DNA (gDNA) contaminating RNA samples is one of them. In gene expression analysis, non-specific amplification due to gDNA contamination will overestimate the abundance of transcript levels and can affect the RT-qPCR results. Generally, gDNA is detected by qRT-PCR using primer pairs annealing to intergenic regions or an intron of the gene of interest. Unfortunately, intron/exon annotations are not yet known for all genes from vertebrate, bacteria, protist, fungi, plant, and invertebrate metazoan species.
Here we present a protocol for detection of gDNA contamination in RNA samples by using ribosomal DNA (rDNA)-based primers. The method is based on the unique features of rDNA: their multigene nature, highly conserved sequences, and high frequency in the genome. Also as a case study, a unique set of primers were designed based on the conserved region of ribosomal DNA (rDNA) in the Poaceae family. The universality of these primer pairs was tested by melt curve analysis and agarose gel electrophoresis. Although our method explains how rDNA-based primers can be applied for the gDNA contamination assay in the Poaceae family,&#160;it could be easily used to other prokaryote and eukaryote species

Here, we present a protocol for tracing genomic DNA (gDNA) contamination in RNA samples. The presented method utilizes primers specific for the internal transcribed spacer region (ITS) of ribosomal DNA (rDNA) genes. The method is suited for reliable and sensitive detection of DNA contamination in most eukaryotes and prokaryotes.

基于核糖体 dna 的 RNA 样本 dna 污染评价

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time ...

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics of these pathogens, in part due to a lack of well-developed genetic and genomic resources. These organisms have large, repetitive genomes, which have made genome sequencing and assembly prohibitively difficult. Here, we describe methods for the collection, extraction, purification and quality control assessment of high molecular weight genomic DNA from one powdery mildew species, Golovinomyces cichoracearum. The protocol described includes mechanical disruption of spores followed by an optimized phenol/chloroform genomic DNA extraction. A typical yield was 7 &#181;g DNA per 150 mg conidia. The genomic DNA that is isolated using this procedure is suitable for long-read sequencing (i.e., &#62; 48.5 kbp). Quality control measures to ensure the size, yield, and purity of the genomic DNA are also described in this method. Sequencing of the genomic DNA of the quality described here will allow for the assembly and comparison of multiple powdery mildew genomes, which in turn will lead to a better understanding and improved control of this agricultural pathogen.

Described here is a method for the extraction, purification, and quality control of genomic DNA from the obligate biotrophic fungal pathogen, powdery mildew, for use in long-read genome sequencing.

超高分子量基因组DNA的分离纯化白粉病对长读测序

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

植物标本标本的鲁棒 DNA 分离与高通量测序库建设

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

有针对性的下一代测序和生物信息学管道评价宪政疾病的遗传决定因素

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

基于纠错 DNA 和 RNA 测序的稀有事件检测方法

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

用于下一代测序的近全长 HIV-1 Proviruses 的扩增

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

利用下一代序列染色体构象捕获 (4 c 序列) 的高通量识别基因调控数列

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

RNA 下一代测序和生物信息学管道, 以识别点对位的表达 Line-1

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...