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Science 13 January 2012:
Vol. 335 no. 6065 pp. 173-174
DOI: 10.1126/science.1216828
Plant Science
+ Author Affiliations
Division of Biological Sciences, Interdisciplinary Plant Group, Missouri Maize Center, University of Missouri, Columbia, MO 65211, USA. E-mail: braundm@missouri.edu
Photosynthesis in plants leads to the accumulation of carbohydrates (e.g., sugars, starch), upon which all terrestrial life depends. In most plants, sucrose is the principal carbohydrate transported long-distance in the veins to support the growth and development of roots, flowers, fruits, and seeds. Sucrose can be directly stored in specialized tissues, such as fruits or the stems of sugarcane and sweet sorghum, or it can be converted into starch in cereal seeds and potato tubers. Thus, proper control of carbohydrate partitioning is fundamental to crop yield and human nutrition and to the development of plant-based biofuels. Given the importance of this process, it may come as a surprise that until now, we did not understand the entire pathway for the export of sucrose from leaves. On page 207 of this issue, Chen et al. (1) identify and characterize the long-sought missing player in sucrose transport, the sucrose effluxer.
植物光合作用导致碳水化合物（如糖，淀粉）的积累，所有陆栖生命皆依赖于此。大多数植物中，蔗糖是长距离运输的最主要碳氺化合物，供应根、花、果实、以及种子的生长与发育。蔗糖可以直接贮存于某些组织，像果实或者甘蔗与甜高粱的茎，也可转化为谷类种子与土豆块茎中的淀粉。因此，适当控制碳水化合物的分配对于作物产量与人体营养，乃至发展基于植物的生物燃料，是很必要的。考虑到此过程的重要性，令人惊奇的是，时至现在，我们依然没有完全理解蔗糖从叶片输出的途径。在本期（科学-335卷-6065期）的第207页，Chen等鉴定并阐明了长久以来所探究的蔗糖运输中缺失角色——蔗糖输出器。
Carbon assimilation in mature leaves results in a surplus of carbohydrates, which are exported through the veins to nonphotosynthetic tissues (2–7). Sucrose is synthesized in leaf mesophyll cells and diffuses cell-to-cell through plasmodesmata (conduits spanning the cell wall and connecting adjacent cells) toward the vein (see the figure). Within the veins, the phloem is the specialized tissue involved in long-distance sucrose transport. The phloem contains three cell types: parenchyma cells, companion cells, and sieve elements. In the majority of crop plants, the companion cells and sieve elements are not connected by plasmodesmata to the other cells in the leaf; therefore, sucrose must be effluxed from the phloem parenchyma cell to the cell wall space (apoplast) before being imported into the companion cells and/or sieve elements by sucrose transporters located on their plasma membranes (2–7). The portions of the sucrose transport pathway from the mesophyll cell to the phloem parenchyma cell, and from the apoplast into the companion cell and sieve element, have been well characterized. However, the mechanism of sucrose efflux into the apoplast, the last unresolved step in the sucrose phloem loading pathway, remained a mystery (8). The sucrose effluxer was finally identified by Chen et al. through an elegant approach that combined cell biology, biochemistry, genomics, and genetics.
成熟叶片中碳的积累引起碳水化合物的盈余，这部分碳氺化合物通过叶脉输出到不能进行光合成的组织。蔗糖合成于叶肉细胞，通过细胞间的胞间连丝（胞间连丝是跨越细胞壁并连接相邻细胞的通道）在扩散至叶脉（见下图）。在叶脉中，韧皮部是负责长距离蔗糖运输的分化组织。韧皮部有三种细胞类型：薄壁细胞、伴细胞和筛管分子。对于大多数作物，伴细胞与筛管分子并没有通过胞间连丝与叶片其它细胞相连；因此，蔗糖必须在进入伴细和/或筛管分子之前通过质膜上的蔗糖转运子，由韧皮部薄壁细胞输出至细胞壁空间。从叶肉细胞到韧皮部薄壁细胞，以及从质外体进入伴细胞和筛管分子，这两部分蔗糖转运途径已被阐明。然而，蔗糖输出至质外体的机制——韧皮部装载蔗糖途径最后一个未解决的步骤，依然是个谜。蔗糖输出子终于被Chen等通过一种简洁的方法，结合细胞生物学、生物化学、基因组学和遗传学，最终被鉴定出。
Sucrose partitioning in plants.
Sucrose is synthesized in leaf mesophyll cells and diffuses through plasmodesmata into phloem parenchyma cells. SWEET proteins facilitate sucrose efflux into the cell wall (apoplast). Sucrose transporters import sucrose into companion cells and/or sieve elements. Sucrose is transported through sieve elements out of leaves to nonphotosynthetic tissues, such as roots, stem, and fruits.
蔗糖合成于叶片叶肉细胞，通过胞间连丝扩散至韧皮部薄壁细胞。SWEET蛋白促进了蔗糖进入细胞壁空间（质外体）。再由蔗糖转运子将蔗糖转运至伴细胞和\或筛管分子。蔗糖经由筛管分子转运出叶片，进入非光合成组织。例如：根部、茎和果实。
CREDIT: Y. HAMMOND/SCIENCE
A key that enabled this breakthrough was the development of fluorescence resonance energy transfer (FRET) optical sensors that could be used in cells to report the sugar concentration in the cytoplasm (9, 10). A sugar-binding protein domain was placed between variants of cyan fluorescent protein and yellow fluorescent protein. When the sensor protein binds sugar, it undergoes a conformational shift that alters the fluorescence, such that a change in the amount of fluorescence emitted can be used to monitor changes in sugar concentration. By expressing such an optical sensor for glucose or sucrose, root cells were observed to rapidly transport the sugars across cellular membranes in response to concentration gradients (11). This led to the hypothesis that novel membrane proteins mediate sugar transport because the expression patterns and biochemical transport properties observed were inconsistent with known sugar transporters.
此项研究突破的关键之处在于应用了荧光共振能量转移（FRET）光学传感器，此技术用于监测胞质蔗糖浓度。一种糖蛋白结合蛋白质功能域被置于青绿色荧光蛋白和黄色荧光蛋白变体之间。传感器蛋白在结合糖类时发生构象变化，此构象变化改变荧光，散发荧光强度的变化可用于监测糖浓度的变化。通过表达这种葡萄糖或者蔗糖光学传感器，可观察到根部细胞经由质膜快速顺浓度梯度转运糖类。从而得出结论：新膜蛋白介导了糖转运，因为表达类型以及所观测到的生化转运特性与已知的糖类转运子不一致。
To identify these unknown proteins, Chen et al. previously used a human cell line to coexpress the glucose sensor and a collection of Arabidopsis proteins containing multiple membrane-spanning domains (12). The authors found that a specific SWEET protein could take up glucose from the cell culture medium. SWEETs are membrane proteins that transport glucose molecules across a membrane down a concentration gradient. Phylogenetic analysis revealed that SWEET genes are evolutionarily conserved from plants to humans. There are 17 SWEET genes in Arabidopsis and 21 in rice. Intriguingly, different bacterial or fungal pathogens obtain carbohydrates from plants by increasing the expression of different plant SWEET genes (12).
为了鉴定这些未知蛋白，Chen等先前使用了人类细胞系，用于共表达葡萄糖传感器与一系列含有多种跨膜功能域的拟南芥蛋白。最终发现一种特异的SWEET蛋白可以从细胞培养基中摄取葡萄糖。SWEET是一种顺浓度梯度转运葡萄糖跨膜的膜蛋白。进化分析表明，SWEET基因从植物到人类进化保守。拟南芥有17个SWEET基因，而水稻有21个。令人好奇的是，某些细菌或者真菌病原体也可通过表达不同的植物SWEET基因来获取碳水化合物。
Chen et al. determined that AtSWEET11 and 12 (and OsSWEET11 and 14 in rice) transport sucrose in Arabidopsis (1). Both transporters localize to the plasma membrane and are expressed in a subset of leaf phloem parenchyma cells, proximal to the companion cells and sieve elements. Mutations in either the AtSWEET11 or 12 genes produced no obvious phenotypes, but double mutants (atsweet11;12) showed moderate defects in sucrose phloem transport and an excessive accumulation of carbohydrates in the leaves. A third gene, AtSWEET13, showed increased expression in the atsweet11;12 double mutant background and may partially compensate for their function. Hence, these SWEET genes are genetically redundant, which likely explains why earlier genetic screens failed to identify the efflux step. Collectively, the data demonstrate that the AtSWEET11 and 12 genes encode the missing link in sucrose phloem loading, the sucrose effluxer.
Chen等测定拟南芥中AtSWEET11与12转运蔗糖（水稻中为OsSWEET11与14）。两种转运子定位于质膜，在一部分靠近伴细胞和筛管分子的叶片韧皮部薄壁细胞中表达。缺失AtSWEET11或12基因的拟南芥突变体没有明显的表型变化。但是双突变体（atsweet11;12）表现出韧皮部蔗糖转运的适度缺陷，以及叶片中碳水化合物的积累。另一个基因，AtSWEET13，在atsweet11;12 双突变体中表达增加，部分补偿了缺失基因功能。因此，这些SWEET基因是遗传性冗余，这很大程度上揭示了先前鉴定输出步骤中遗传筛选的失败。这些数据共同证实了AtSWEET11和12基因编码韧皮部蔗糖装载中缺失的一环——蔗糖输出子。
The identification of SWEET proteins as sucrose facilitators raises a number of questions. Are the regulation and localization of SWEETs and sucrose transporters coordinated to maximize phloem loading efficiency and minimize any potential loss of sucrose to the apoplast (and thereby to pathogens)? Additionally, AtSWEET11 and 12 are expressed in most Arabidopsis tissues; what other roles beyond phloem loading might they play? One possibility is that they may function in sucrose efflux to seeds (13). Another is that during long-distance transport, SWEETs may facilitate the “leakage” of sucrose from the phloem to nourish adjacent stem tissues (14). If so, manipulating SWEET expression could enhance carbohydrate delivery to developing seeds to increase yield, or it could increase the sucrose concentration in the storage cells of sugarcane or sweet sorghum stems to improve biofuel production.
SWEET蛋白作为蔗糖输出子的鉴定引起了若干问题。SWEET与蔗糖转运子的调节与定位对于最大化韧皮部装在效率以及最小化向质外体（或者病原体）运输的潜在蔗糖损失相协调？另外，AtSWEET11和12 表达于拟南芥多数组织中；除了韧皮部装载其它的作用是什么？一种可能是它们可能在蔗糖输出至种子过程起作用。另一种可能是，在长距离运输中，SWEET可能促进蔗糖由韧皮部的“渗漏”，以便滋养相邻的茎组织。如果是这样，控制SWEET表达可以增加碳水化合物向正在发育的种子的传递，以增加产量，或者，也可以增加甘蔗或者甜高粱贮存细胞中蔗糖浓度，以改善生物燃料生产。
（译者言：待续）
转载请注明转载自本博客。。。。。。。。。
In a monumental breakthrough with far ranging implications, cardiologists at the Children’s Hospital Boston in Massachusetts have kept suffocating rabbits alive for 15 minutes with injections of oxygen-filled microparticles. The groundbreaking procedure could conceivably prevent millions of deaths each year caused by such things as heart attacks and choking.
Developed by John Kheir and his team at CHB, the technique could prevent heart attacks and any kind of injury caused by oxygen deprivation once it’s proven safe for human use. It might also be used to avoid cerebral palsy which is caused by a compromised fetal blood supply. Assuming the procedure could ever be developed for use outside of clinical settings, people could use it in the event of emergencies. It could also become part of a first aid kit in anticipation of problems -– a welcome breakthrough for scuba divers and high altitude climbers.
The researchers also note that the technique could be used to augment oxygen delivery to at-risk organs, while opening the door to entirely new diagnostic techniques.
Outside of medical use, the procedure could also impact on normal human functioning. Swimmers and divers could conceivably take the injections and stay underwater for unprecedented lengths of time.
Writing in the journal Nature, Duncan Graham-Rowe explains how it was done:
The microcapsules used by Kheir and his team…consist of single-layer spherical shells of biological molecules called lipids, each surrounding a small bubble of oxygen gas. The gaseous oxygen is thus encapsulated and suspended in a liquid emulsion, so can’t form larger bubbles.
The particles are injected directly into the bloodstream, where they mingle with circulating red blood cells. The oxygen diffuses into the cells within seconds of contact, says Kheir. “By the time the microparticles get to the lungs, the vast majority of the oxygen has been transferred to the red blood cells,” he says. This distinguishes these microcapsules from the various forms of artificial blood currently in use, which can carry oxygen around the body, but must still receive it from the lungs.
In the experiment, the rabbits underwent 15 minutes of complete tracheal occlusion (their air passageways were blocked), but showed no ill effects during the procedure.
Looking ahead, Kheir noted that a primary advantage of his technique is how fast it goes to work. Graham-Rowe writes:
He thinks that it might be possible to modify the technique to keep subjects alive for as much as 30 minutes, but doubts that it could be pushed much further. Because the microparticles do not recirculate, it would be necessary to continuously infuse fresh ones into the blood, and there are limits to how much extra fluid can be pumped into the bloodstream. “It’s not going to replace the lungs, it just replaces their function for a limited period of time,” says Kheir.RPKM, Reads Per Kilobase of exon model per Million mapped reads, is defined in this way [Mortazavi et al., 2008]: 
.
————————————————————————————————————————————————————-
RNA-seq是透过次世代定序的技术来侦测基因表现量的方法，在衡量基因表现量时，若是单纯以map到的read数来计算基因的表现量，在统计上是一件相当不合理事，因为在随机抽样的情况下，序列较长的基因被抽到的机率本来就会比序列短的基因较高，如此一来，序列长的基因永远会被认为表现量较高，而错估基因真正的表现量，所以Ali Mortazavi等人在2008年提出以RPKM在估计基因的表现量。
RPKM是将map到基因的read数除以map到genome的所有read数(以million为单位)与RNA的长度(以KB为单位)。
其公式为:
其中，total exon reads / mapped reads (millions) 可以视为所有read 数中有百分之多少是map 到这个基因，然后再除以基因长度，就可以某基因得到单位长度有百分之多少的total mapped read 有表现。
以下就用一个简化的例子来说明RPKM的运用方式与概念:
假设一基因体只有两个基因，一个9 KB，一个1 KB，如今有一sample，其map 到9 KB 的read 有18 million 个，map 到1 KB 的有2 million 个，如下图所示。
对于9 KB 的基因而言，
Total exon reads=18 million
Mapped reads=18+2=20 million
Exon length=9 KB
RPKM =18/(20*9)=0.1
对于1 KB 的基因而言，
Total exon reads=2 million
Mapped reads=18+2=20 million
Exon length=1 KB
RPKM =2/(20*1)=0.1
由此我们可以知道这两个基因表现量没有差别。
假设此时我们有另一个sample，其表现如下图所示:
我们可以发现此sample中9 KB基因的read数明显比上一个sample少，如果我们计算RPKM可以得到RPKM = 9/((9+1)*9)=0.1，却与上一个sample相同，这可能是因为cDNA浓度较低或是其他sample备制过程的问题，造成整体read变少，但是对9 KB基因而言，其read数占所有read数的比例并没有发生改变，所以其表现量会和上一个sample相同。
Shaokui Wang, Kun Wu, Qingbo Yuan, Xueying Liu, Zhengbin Liu,
Xiaoyan Lin, Ruizhen Zeng, Haitao Zhu, Guojun Dong, Qian Qian, Guiquan Zhang & Xiangdong Fu
Nature Genetics (2012) doi:10.1038/ng.2327
The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, National Centre for Plant Gene Research, Beijing, China.
Shaokui Wang, Kun Wu, Qingbo Yuan, Xueying Liu, Zhengbin Liu & Xiangdong Fu
The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China.
Shaokui Wang, Xiaoyan Lin, Ruizhen Zeng, Haitao Zhu & Guiquan Zhang
The State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Guojun Dong & Qian Qian
S.W. performed most of the experiments. R.Z. and X. Lin developed the SSSLs and conducted QTL analysis. Q.Y. and H.Z. developed the NILs. X. Liu and Z.L. performed rice transformation. G.D. and K.W. performed field experiments. Q.Q. and K.W. analyzed grain quality. G.Z. and X.F. supervised this study. X.F. designed the experiments and wrote the manuscript. All authors discussed the results and contributed to the drafting of the manuscript.
Grain size and shape are important components of grain yield and quality and have been under selection since cereals were first domesticated. Here, we show that a quantitative trait locus GW8 is synonymous with OsSPL16, which encodes a protein that is a positive regulator of cell proliferation. Higher expression of this gene promotes cell division and grain filling, with positive consequences for grain width and yield in rice. Conversely, a loss-of-function mutation in Basmati rice is associated with the formation of a more slender grain and better quality of appearance. The correlation between grain size and allelic variation at the GW8 locus suggests that mutations within the promoter region were likely selected in rice breeding programs. We also show that a marker-assisted strategy targeted at elite alleles of GS3 and OsSPL16 underlying grain size and shape can be effectively used to simultaneously improve grain quality and yield.
谷粒大小与形状是大米产量与质量的重要因素，自谷类驯化始起就一直处于育种选择压之下。在此，我们表明一个QTL，GW8，与OsSPL6同义，编码一种细胞增殖正调因子。此基因的高度表达促使细胞分裂与灌浆，与谷粒宽度和水稻产量正相关。相反，在Basmati水稻突变体中，此基因的功能缺失导致谷粒更为细长，以及更好的表观品质。谷粒大小与GW8位点处等位基因变异的相关性表明，启动子区的突变很可能是在水稻育种过程中所选择。同时，我们也表明，导向影响谷粒大小与形状的GS3与OsSPL6优良等位基因的标记辅助育种可以有效地应用于同时改善水稻质量与产量。
Endnote citation/引文:
PDF document/文档
Control of grain size, shape and quality by OsSPL16 in rice.PDF
Supplementary file/附加材料
Rice field.
Have no idea which kind of plant.
How beautiful these flowers are!
Looking far into the distance from my university campus. Splendid scenery. Wo…….
张无忌放弃了江湖与江山
他把幸福给了赵敏
却把牵挂给了小昭
把漂泊给了蛛儿
把憾恨给了芷若……
杨过和小龙女最终做了神仙眷侣
也许他知道，也许他不知道
也许他装作不知道
程英和陆无双为他负尽青春抛尽韶华
郭襄为他天涯思君念念不忘
也许他记得，也许他不记得
曾经有一个叫公孙绿萼的姑娘把一生停驻在他一刹那的目光里
而他所能给的，也只能是一曲清箫、三枚金针或者某一刻的眷顾而已
这世间，太少的相濡以沫，太多的相忘江湖……
我们曾经深深地爱过一些人
爱的时候，把朝朝暮暮当作天长地久
把缱绻一时当作被爱了一世
于是承诺，于是奢望执子之手，幸福终老
然后一切消失了，然后我们终于明白
天长地久是一件多么可遇不可求的事情
幸福是一种多么玄妙多么脆弱的东西
也许爱情与幸福无关
也许这一生最终的幸福与心底最深处的那个人无关
也许将来的某一天，我们会牵住谁的手，一生细水长流地把风景看透。
其实承诺并没有什么，不见了也不算什么
所有的一切自有它的归宿
我们学着看淡，学着不强求
学着深藏，把你深深埋藏
藏到岁月的烟尘企及不到的地方……
只是，只是为什么在某个落雨的黄昏
在某个寂寂的夜里你还是隐隐地在我心里淡入、淡出
淡出、淡入，拿不走，抹不掉。
阿朱如花的笑靥正在青石桥旁小镜湖边渐渐凋零
乔峰在滂沱的夜雨中泪雨也滂沱
你给我保护，我还你祝福
你英雄好汉需要抱负
可你欠我幸福，拿什么来弥补
陈家洛不愿负天下人，便负红颜
一个为他香消玉殒，一个因他寂寞余生
也许他的命运早早已是注定
终是塞上牛羊空许约。
空许约，空许约，幸福永远未完成……
我多么想和你有一个深深的拥抱之后,转身离去。
情深未变却寒盟
终究差了那么一点点
幸福转眼消逝
从此一个人，日日自己关门
一个人熄灯
其实也没什么不好，只不过寒冷的夜里少了一个人的温暖
只不过幸福不再完整……
倚天的结局处，周芷若曾这样问过张无忌，在小昭、蛛儿、赵敏和她四个人中，他真正爱的人是哪一个。张无忌一时感慨万千，想起自己也曾扪心自问过，那时只觉得，若能和四位姑娘一起长相厮守，岂不逍遥快活？然而世事变迁,小昭远赴波斯做了教主，表妹蛛儿逝世，芷若误入歧途，只有赵姑娘一直陪在自己身边。虽然期间曾产生过误会，他对赵敏是又爱又恨，但心里从未放下过对她的牵念。然而，芷若的介入总让他内心摇摆不定，始终无法正视自己的情感。直到这一刻，面对赵敏的不辞而别，他终于发现，自己对那鬼灵精怪的小妖女竟这般难以割舍，若是今生再见不了她，自己也决计活不下去了。他终于找到心底的那个答案，对芷若，他是一向敬重，对蛛儿，他是心生感激，对小昭，他是意存怜惜，但对赵敏，却是刻骨铭心的相爱。
人有时候，总在失去时才后知后觉，一些人，一些事，以为只是生命中一抹浮云，以为可以从此相忘于江湖，却在别离之际发现，那些过往原来早已扎根在心底，拿不掉，抹不去。
杨过与小龙女终成眷属，逍遥于江湖之外，他可记得，还有一个痴心的女子对他天涯思君念念不忘，抛尽韶华，守候一生。他是否会想起，多年前那张天真无邪的面容，是否会想起，初遇时她莞尔一笑，道，我姓郭，单名一个襄字。
眷恋的人，给不了你承诺，于是你终于明白，幸福是一件多么可遇不可求的事情。可是为何仍要飞蛾扑火，执着一生？也许就如李莫愁时常低吟的那样：问世间情为何物，直教人生死相许。这世界上最复杂的东西，一个人又如何能想的透彻？
有一个人，教会你如何去爱了，但是，他却不爱你了
有一个人，你一直在等他，他却忘记了你。
有一个人，他想离开了，你却没有丝毫挽留，因为你渐渐明白，挽留是没有用的，你能给的，只有自由。
你以为只要走的很潇洒，就不会有太多的痛苦，就不会有留恋，可是，为什么在喧闹的人群中会突然沉默下来，为什么听歌听到一半会突然哽咽不止。你不知道自己在期待什么，不知道自己在坚持什么，脑海里挥之不去的，都是过往的倒影。
爱你的人，对你的要求很少，可以在很想你的时候看看你，可以在寂寞的时候和你说句话，这就是她所有的幸福。
如果因为执念而作出仓促的决定，可以离开，但请不要走远，不要急着为彼此定性，不要急着分清界限， 回头看看，她是否还在。
善待爱你的那个人，那个不希望你困扰，所以强颜欢笑、骗你说释怀了的人，那个默默关注你，从不曾离开的人，如果你还彷徨着，如果你还抑制不住的想着她，如果你还在意她的一颦一簇，不妨给她一个可能，也给自己一个可能。
在爱情里，如果两人都很被动，一段美好的姻缘不免在时间的摧残下消磨殆尽，并不是两个人不适合，而是双方都习惯于等待，等待对方先主动，没有耐心的人于是选择离开，最后徒留遗憾。
所以，爱是有来生的，就像不灭的火种，只需加点干柴，它依旧能发出夺目的火光。
几米说：
当你喜欢我的时候，我不喜欢你
当你爱上我的时候，我喜欢上你
当你离开我的时候，我却爱上你
是你走的太快，还是我跟不上你的脚步？
我们错过了诺亚方舟，错过了泰坦尼克号，错过了一切惊险与不惊险，我们还要继续错过
但是，请允许我说这样自私的话
多年后
你若未嫁
我若未娶
那
我们能不能在一起？？？？
记住你欠我的幸福!!
高通量测序技术是对传统测序一次革命性的改变，一次对几十万到几百万条DNA分子进行序列测定，因此在有些文献中称其为下一代测序技术(next generation sequencing)足见其划时代的改变，同时高通量测序使得对一个物种的转录组和基因组进行细致全貌的分析成为可能，所以又被称为深度测序(deep sequencing)。
de novo测序也称为从头测序：其不需要任何现有的序列资料就可以对某个物种进行测序，利用生物信息学分析手段对序列进行拼接，组装，从而获得该物种的基因组图谱。获得一个物种的全基因组序列是加快对此物种了解的重要捷径。随着新一代测序技术的飞速发展，基因组测序所需的成本和时间较传统技术都大大降低，大规模基因组测序渐入佳境，基因组学研究也迎来新的发展契机和革命性突破。利用新一代高通量、高效率测序技术以及强大的生物信息分析能力，可以高效、低成本地测定并分析所有生物的基因组序列。
全基因组重测序是对基因组序列已知的个体进行基因组测序，并在个体或群体水平上进行差异性分析的方法。随着基因组测序成本的不断降低，人类疾病的致病突变研究由外显子区域扩大到全基因组范围。通过构建不同长度的插入片段文库和短序列、双末端测序相结合的策略进行高通量测序，实现在全基因组水平上检测疾病关联的常见、低频、甚至是罕见的突变位点，以及结构变异等，具有重大的科研和产业价值。
外显子组测序是指利用序列捕获技术将全基因组外显子区域DNA捕捉并富集后进行高通量测序的基因组分析方法。
Small RNA（micro RNAs、siRNAs和 pi RNAs）是生命活动重要的调控因子，在基因表达调控、生物个体发育、代谢及疾病的发生等生理过程中起着重要的作用。Illumina能够对细胞或者组织中的全部Small RNA进行深度测序及定量分析等研究。实验时首先将18-30 nt范围的Small RNA从总RNA中分离出来，两端分别加上特定接头后体外反转录做成cDNA再做进一步处理后，利用测序仪对DNA片段进行单向末端直接测序。通过Illumina对Small RNA大规模测序分析，可以从中获得物种全基因组水平的miRNA图谱，实现包括新miRNA分子的挖掘，其作用靶基因的预测和鉴定、样品间差异表达分析、miRNAs聚类和表达谱分析等科学应用。
染色质免疫共沉淀技术（Chromatin Immunoprecipitation，ChIP）也称结合位点分析法，是研究体内蛋白质与DNA相互作用的有力工具，通常用于转录因子结合位点或组蛋白特异性修饰位点的研究。将ChIP与第二代测序技术相结合的ChIP-Seq技术，能够高效地在全基因组范围内检测与组蛋白、转录因子等互作的DNA区段。
ChIP-Seq的原理是：首先通过染色质免疫共沉淀技术（ChIP）特异性地富集目的蛋白结合的DNA片段，并对其进行纯化与文库构建；然后对富集得到的DNA片段进行高通量测序。研究人员通过将获得的数百万条序列标签精确定位到基因组上，从而获得全基因组范围内与组蛋白、转录因子等互作的DNA区段信息。
基因表达谱(gene expression profile)：指通过构建处于某一特定状态下的细胞或组织的非偏性cDNA文库,大规模cDNA测序,收集cDNA序列片段、定性、定量分析其mRNA群体组成,从而描绘该特定细胞或组织在特定状态下的基因表达种类和丰度信息,这样编制成的数据表就称为基因表达谱
成熟的microRNA（miRNA）是17~24nt的单链非编码RNA分子，通过与mRNA相互作用影响目标mRNA的稳定性及翻译，最终诱导基因沉默，调控着基因表达、细胞生长、发育等生物学过程。基于第二代测序技术的microRNA测序，可以一次性获得数百万条microRNA序列，能够快速鉴定出不同组织、不同发育阶段、不同疾病状态下已知和未知的microRNA及其表达差异，为研究microRNA对细胞进程的作用及其生物学影响提供了有力工具。
转录组学（transcriptomics）是在基因组学后新兴的一门学科，即研究特定细胞在某一功能状态下所能转录出来的所有RNA（包括mRNA和非编码RNA）的类型与拷贝数。Illumina提供的mRNA测序技术可在整个mRNA领域进行各种相关研究和新的发现。mRNA测序不对引物或探针进行设计，可自由提供关于转录的客观和权威信息。研究人员仅需要一次试验即可快速生成完整的poly-A尾的RNA完整序列信息，并分析基因表达、cSNP、全新的转录、全新异构体、剪接位点、等位基因特异性表达和罕见转录等最全面的转录组信息。简单的样品制备和数据分析软件支持在所有物种中的mRNA测序研究。
功能基因组学（Functuional genomics）又往往被称为后基因组学（Postgenomics），它利用结构基因组所提供的信息和产物，发展和应用新的实验手段，通过在基因组或系统水平上全面分析基因的功能，使得生物学研究从对单一基因或蛋白质得研究转向多个基因或蛋白质同时进行系统的研究。这是在基因组静态的碱基序列弄清楚之后转入对基因组动态的生物学功能学研究。研究内容包括基因功能发现、基因表达分析及突变检测。基因的功能包括：生物学功能，如作为蛋白质激酶对特异蛋白质进行磷酸化修饰；细胞学功能，如参与细胞间和细胞内信号传递途径；发育上功能，如参与形态建成等。采用的手段包括经典的减法杂交，差示筛选，cDNA代表差异分析以及mRNA差异显示等，但这些技术不能对基因进行全面系统的分析，新的技术应运而生，包括基因表达的系统分析（serial analysis of gene expression,SAGE），cDNA微阵列（cDNA microarray），DNA 芯片（DNA chip）和序列标志片段显示（sequence tagged fragments display。
比较基因组学(Comparative Genomics)是基于基因组图谱和测序基础上，对已知的基因和基因组结构进行比较，来了解基因的功能、表达机理和物种进化的学科。利用模式生物基因组与人类基因组之间编码顺序上和结构上的同源性，克隆人类疾病基因，揭示基因功能和疾病分子机制，阐明物种进化关系，及基因组的内在结构。
表观遗传学是研究基因的核苷酸序列不发生改变的情况下，基因表达了可遗传的变化的一门遗传学分支学科。表观遗传的现象很多，已知的有DNA甲基化（DNA methylation），基因组印记（genomic impriting），母体效应（maternal effects），基因沉默（gene silencing），核仁显性，休眠转座子激活和RNA编辑（RNA editing）等。
计算生物学是指开发和应用数据分析及理论的方法、数学建模、计算机仿真技术等。当前，生物学数据量和复杂性不断增长，每14个月基因研究产生的数据就会翻一番，单单依靠观察和实验已难以应付。因此，必须依靠大规模计算模拟技术，从海量信息中提取最有用的数据。
基因组印记(又称遗传印记)是指基因根据亲代的不同而有不同的表达。印记基因的存在能导致细胞中两个等位基因的一个表达而另一个不表达。基因组印记是一正常过程，此现象在一些低等动物和植物中已发现多年。印记的基因只占人类基因组中的少数，可能不超过5%，但在胎儿的生长和行为发育中起着至关重要的作用。基因组印记病主要表现为过度生长、生长迟缓、智力障碍、行为异常。目前在肿瘤的研究中认为印记缺失是引起肿瘤最常见的遗传学因素之一。
基因组学（英文genomics），研究生物基因组和如何利用基因的一门学问。用于概括涉及基因作图、测序和整个基因组功能分析的遗传学分支。该学科提供基因组信息以及相关数据系统利用，试图解决生物，医学，和工业领域的重大问题。
DNA甲基化是指在DNA甲基化转移酶的作用下，在基因组CpG二核苷酸的胞嘧啶5’碳位共价键结合一个甲基基团。正常情况下，人类基因组“垃圾”序列的CpG二核苷酸相对稀少，并且总是处于甲基化状态，与之相反，人类基因组中大小为100—1000 bp左右且富含CpG二核苷酸的CpG岛则总是处于未甲基化状态，并且与56％的人类基因组编码基因相关。人类基因组序列草图分析结果表明，人类基因组CpG岛约为28890个，大部分染色体每1 Mb就有5—15个CpG岛，平均值为每Mb含10．5个CpG岛，CpG岛的数目与基因密度有良好的对应关系[9]。由于DNA甲基化与人类发育和肿瘤疾病的密切关系，特别是CpG岛甲基化所致抑癌基因转录失活问题，DNA甲基化已经成为表观遗传学和表观基因组学的重要研究内容。
基因组注释(Genome annotation) 是利用生物信息学方法和工具,对基因组所有基因的生物学功能进行高通量注释,是当前功能基因组学研究的一个热点。基因组注释的研究内容包括基因识别和基因功能注释两个方面。基因识别的核心是确定全基因组序列中所有基因的确切位置。
Sanger法测序利用一种DNA聚合酶来延伸结合在待定序列模板上的引物。直到掺入一种链终止核苷酸为止。每一次序列测定由一套四个单独的反应构成，每个反应含有所有四种脱氧核苷酸三磷酸(dNTP)，并混入限量的一种不同的双脱氧核苷三磷酸(ddNTP)。由于ddNTP缺乏延伸所需要的3-OH基团，使延长的寡聚核苷酸选择性地在G、A、T或C处终止。终止点由反应中相应的双脱氧而定。每一种dNTPs和ddNTPs的相对浓度可以调整，使反应得到一组长几百至几千碱基的链终止产物。它们具有共同的起始点，但终止在不同的的核苷酸上，可通过高分辨率变性凝胶电泳分离大小不同的片段，凝胶处理后可用X-光胶片放射自显影或非同位素标记进行检测。
This page is designed to be a cheat sheet answering the most basic and fundamental questions that commonly come up when using R. Note that it is by no means intended to be an exhaustive list of the commonly used R functions.
Q: What is a package?
A package is a collection or group of objects that R can use. A package may contain functions, data frames, or other objects, such as dynamically loaded libraries of compiled code.
Q: How do I see which packages I have available?
library()
Q: How do I load a package?
library(“package_name”)
Q: How do I see the documentation for a particular package?
library(help=”package_name”)
help(package=”package_name”)
Q: How do I see the help file for a specific function?
help(“function_name”) ?function_name
Q: How can I save my work?
You can save all the objects and functions that you have created in an .RData file, by using the save or the save.image functions. It is very important that you remember to include the .RData extension when indicating the file path because R will not supply it for you!
save(file=”d:/file_name.RData”)
save.image(“d:/file_name.RData”)
On a PC you can also access this through the file menu: File Save workspace browse to the folder where you want to save the file and supply the file name of your choice
Q: How can I retrieve the work that I have saved using a save.image function?
The load function will load an .RData file.
load(“d:/file_name.RData”)
On a PC you can also access this through the file menu: File Load workspace browse to the folder where you saved the .RData file and click open
Q: How do I save all the commands that I have used in an R session?
You can save a history of your R session in an .Rhistory file by using the history function. It is very important that you remember to include the .Rhistory extension when indicating the file path because R will not supply it for you!
history(“d:/file_name.Rhistory”)
On a PC you can also access this through the file menu: File Save history browse to the folder where you want to save the file and supply the file name of your choice
Q: How can I retrieve the work that I have saved using a history function?
The loadhistory function will load an .Rhistory file.
loadhistory(“d:/file_name.Rhistory”)
On a PC you can also access this through the file menu: File Load history browse to the folder where you saved the .Rhistory file and click open
Q: How do I use a script of commands and functions saved in a text file?
source(“d:/file_name.txt”)
Q: How do I get R to echo back the commands and functions in a script that I am sourcing into R? For example, if I have written functions and I want to see the functions being executed.
source(“d:/file_name.txt”, echo=T)
Q: How do I close the help file when working on a Macintosh?
Typing q will close the help file and bring you back to the console.
Q: How can I see a list of the objects that are currently available?
objects() ls()
Q: How do I remove unwanted objects and functions?
rm(object_name1, object_names2, etc.)
rm(function_name1, function_name2, etc.)
Q: What is the first thing to check if a function or object is behaving strangely and unexpectedly?
Always check if there is a masked object or function which is being used instead of the object or function that you intended to use. If this is the case then the object or function can be removed by using the rm function as shown in the previous answer.
masked()
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